the main task of a problem solving agent is

Problem Solving Agents in Artificial Intelligence

In this post, we will talk about Problem Solving agents in Artificial Intelligence, which are sort of goal-based agents. Because the straight mapping from states to actions of a basic reflex agent is too vast to retain for a complex environment, we utilize goal-based agents that may consider future actions and the desirability of outcomes.

You Will Learn

Problem Solving Agents

Problem Solving Agents decide what to do by finding a sequence of actions that leads to a desirable state or solution.

An agent may need to plan when the best course of action is not immediately visible. They may need to think through a series of moves that will lead them to their goal state. Such an agent is known as a problem solving agent , and the computation it does is known as a search .

The problem solving agent follows this four phase problem solving process:

• Goal Formulation: This is the first and most basic phase in problem solving. It arranges specific steps to establish a target/goal that demands some activity to reach it. AI agents are now used to formulate goals.
• Problem Formulation: It is one of the fundamental steps in problem-solving that determines what action should be taken to reach the goal.
• Search: After the Goal and Problem Formulation, the agent simulates sequences of actions and has to look for a sequence of actions that reaches the goal. This process is called search, and the sequence is called a solution . The agent might have to simulate multiple sequences that do not reach the goal, but eventually, it will find a solution, or it will find that no solution is possible. A search algorithm takes a problem as input and outputs a sequence of actions.
• Execution: After the search phase, the agent can now execute the actions that are recommended by the search algorithm, one at a time. This final stage is known as the execution phase.

Problems and Solution

Before we move into the problem formulation phase, we must first define a problem in terms of problem solving agents.

A formal definition of a problem consists of five components:

Initial State

Transition model.

It is the agent’s starting state or initial step towards its goal. For example, if a taxi agent needs to travel to a location(B), but the taxi is already at location(A), the problem’s initial state would be the location (A).

It is a description of the possible actions that the agent can take. Given a state s, Actions ( s ) returns the actions that can be executed in s. Each of these actions is said to be appropriate in s.

It describes what each action does. It is specified by a function Result ( s, a ) that returns the state that results from doing action an in state s.

The initial state, actions, and transition model together define the state space of a problem, a set of all states reachable from the initial state by any sequence of actions. The state space forms a graph in which the nodes are states, and the links between the nodes are actions.

It determines if the given state is a goal state. Sometimes there is an explicit list of potential goal states, and the test merely verifies whether the provided state is one of them. The goal is sometimes expressed via an abstract attribute rather than an explicitly enumerated set of conditions.

It assigns a numerical cost to each path that leads to the goal. The problem solving agents choose a cost function that matches its performance measure. Remember that the optimal solution has the lowest path cost of all the solutions .

Example Problems

The problem solving approach has been used in a wide range of work contexts. There are two kinds of problem approaches

• Standardized/ Toy Problem: Its purpose is to demonstrate or practice various problem solving techniques. It can be described concisely and precisely, making it appropriate as a benchmark for academics to compare the performance of algorithms.
• Real-world Problems: It is real-world problems that need solutions. It does not rely on descriptions, unlike a toy problem, yet we can have a basic description of the issue.

Some Standardized/Toy Problems

Vacuum world problem.

Let us take a vacuum cleaner agent and it can move left or right and its jump is to suck up the dirt from the floor.

The vacuum world’s problem can be stated as follows:

States: A world state specifies which objects are housed in which cells. The objects in the vacuum world are the agent and any dirt. The agent can be in either of the two cells in the simple two-cell version, and each call can include dirt or not, therefore there are 2×2×2 = 8 states. A vacuum environment with n cells has n×2 n states in general.

Initial State: Any state can be specified as the starting point.

Actions: We defined three actions in the two-cell world: sucking, moving left, and moving right. More movement activities are required in a two-dimensional multi-cell world.

Transition Model: Suck cleans the agent’s cell of any filth; Forward moves the agent one cell forward in the direction it is facing unless it meets a wall, in which case the action has no effect. Backward moves the agent in the opposite direction, whilst TurnRight and TurnLeft rotate it by 90°.

Goal States: The states in which every cell is clean.

Action Cost: Each action costs 1.

8 Puzzle Problem

In a sliding-tile puzzle , a number of tiles (sometimes called blocks or pieces) are arranged in a grid with one or more blank spaces so that some of the tiles can slide into the blank space. One variant is the Rush Hour puzzle, in which cars and trucks slide around a 6 x 6 grid in an attempt to free a car from the traffic jam. Perhaps the best-known variant is the 8- puzzle (see Figure below ), which consists of a 3 x 3 grid with eight numbered tiles and one blank space, and the 15-puzzle on a 4 x 4  grid. The object is to reach a specified goal state, such as the one shown on the right of the figure. The standard formulation of the 8 puzzles is as follows:

STATES : A state description specifies the location of each of the tiles.

INITIAL STATE : Any state can be designated as the initial state. (Note that a parity property partitions the state space—any given goal can be reached from exactly half of the possible initial states.)

ACTIONS : While in the physical world it is a tile that slides, the simplest way of describing action is to think of the blank space moving Left , Right , Up , or Down . If the blank is at an edge or corner then not all actions will be applicable.

TRANSITION MODEL : Maps a state and action to a resulting state; for example, if we apply Left to the start state in the Figure below, the resulting state has the 5 and the blank switched.

GOAL STATE :  It identifies whether we have reached the correct goal state. Although any state could be the goal, we typically specify a state with the numbers in order, as in the Figure above.

ACTION COST : Each action costs 1.

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Problem-Solving Agents In Artificial Intelligence

In artificial intelligence, a problem-solving agent refers to a type of intelligent agent designed to address and solve complex problems or tasks in its environment. These agents are a fundamental concept in AI and are used in various applications, from game-playing algorithms to robotics and decision-making systems. Here are some key characteristics and components of a problem-solving agent:

• Perception : Problem-solving agents typically have the ability to perceive or sense their environment. They can gather information about the current state of the world, often through sensors, cameras, or other data sources.
• Knowledge Base : These agents often possess some form of knowledge or representation of the problem domain. This knowledge can be encoded in various ways, such as rules, facts, or models, depending on the specific problem.
• Reasoning : Problem-solving agents employ reasoning mechanisms to make decisions and select actions based on their perception and knowledge. This involves processing information, making inferences, and selecting the best course of action.
• Planning : For many complex problems, problem-solving agents engage in planning. They consider different sequences of actions to achieve their goals and decide on the most suitable action plan.
• Actuation : After determining the best course of action, problem-solving agents take actions to interact with their environment. This can involve physical actions in the case of robotics or making decisions in more abstract problem-solving domains.
• Feedback : Problem-solving agents often receive feedback from their environment, which they use to adjust their actions and refine their problem-solving strategies. This feedback loop helps them adapt to changing conditions and improve their performance.
• Learning : Some problem-solving agents incorporate machine learning techniques to improve their performance over time. They can learn from experience, adapt their strategies, and become more efficient at solving similar problems in the future.

Problem-solving agents can vary greatly in complexity, from simple algorithms that solve straightforward puzzles to highly sophisticated AI systems that tackle complex, real-world problems. The design and implementation of problem-solving agents depend on the specific problem domain and the goals of the AI application.

Hello, I’m Hridhya Manoj. I’m passionate about technology and its ever-evolving landscape. With a deep love for writing and a curious mind, I enjoy translating complex concepts into understandable, engaging content. Let’s explore the world of tech together

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Problem Solving in Artificial Intelligence

The reflex agent of AI directly maps states into action. Whenever these agents fail to operate in an environment where the state of mapping is too large and not easily performed by the agent, then the stated problem dissolves and sent to a problem-solving domain which breaks the large stored problem into the smaller storage area and resolves one by one. The final integrated action will be the desired outcomes.

On the basis of the problem and their working domain, different types of problem-solving agent defined and use at an atomic level without any internal state visible with a problem-solving algorithm. The problem-solving agent performs precisely by defining problems and several solutions. So we can say that problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.  

We can also say that a problem-solving agent is a result-driven agent and always focuses on satisfying the goals.

There are basically three types of problem in artificial intelligence:

1. Ignorable: In which solution steps can be ignored.

2. Recoverable: In which solution steps can be undone.

3. Irrecoverable: Solution steps cannot be undo.

Steps problem-solving in AI: The problem of AI is directly associated with the nature of humans and their activities. So we need a number of finite steps to solve a problem which makes human easy works.

These are the following steps which require to solve a problem :

• Problem definition: Detailed specification of inputs and acceptable system solutions.
• Problem analysis: Analyse the problem thoroughly.
• Knowledge Representation: collect detailed information about the problem and define all possible techniques.
• Problem-solving: Selection of best techniques.

Components to formulate the associated problem: 

• Initial State: This state requires an initial state for the problem which starts the AI agent towards a specified goal. In this state new methods also initialize problem domain solving by a specific class.
• Action: This stage of problem formulation works with function with a specific class taken from the initial state and all possible actions done in this stage.
• Transition: This stage of problem formulation integrates the actual action done by the previous action stage and collects the final stage to forward it to their next stage.
• Goal test: This stage determines that the specified goal achieved by the integrated transition model or not, whenever the goal achieves stop the action and forward into the next stage to determines the cost to achieve the goal.  
• Path costing: This component of problem-solving numerical assigned what will be the cost to achieve the goal. It requires all hardware software and human working cost.

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Examples of Problem Solving Agents in Artificial Intelligence

In the field of artificial intelligence, problem-solving agents play a vital role in finding solutions to complex tasks and challenges. These agents are designed to mimic human intelligence and utilize a range of algorithms and techniques to tackle various problems. By analyzing data, making predictions, and finding optimal solutions, problem-solving agents demonstrate the power and potential of artificial intelligence.

One example of a problem-solving agent in artificial intelligence is a chess-playing program. These agents are capable of evaluating millions of possible moves and predicting the best one to make based on a wide array of factors. By utilizing advanced algorithms and machine learning techniques, these agents can analyze the current state of the game, anticipate future moves, and make strategic decisions to outplay even the most skilled human opponents.

Another example of problem-solving agents in artificial intelligence is autonomous driving systems. These agents are designed to navigate complex road networks, make split-second decisions, and ensure the safety of both passengers and pedestrians. By continuously analyzing sensor data, identifying obstacles, and calculating optimal paths, these agents can effectively solve problems related to navigation, traffic congestion, and collision avoidance.

Definition and Importance of Problem Solving Agents

A problem solving agent is a type of artificial intelligence agent that is designed to identify and solve problems. These agents are programmed to analyze information, develop potential solutions, and select the best course of action to solve a given problem.

Problem solving agents are an essential aspect of artificial intelligence, as they have the ability to tackle complex problems that humans may find difficult or time-consuming to solve. These agents can handle large amounts of data and perform calculations and analysis at a much faster rate than humans.

Problem solving agents can be found in various domains, including healthcare, finance, manufacturing, and transportation. For example, in healthcare, problem solving agents can analyze patient data and medical records to diagnose diseases and recommend treatment plans. In finance, these agents can analyze market trends and make investment decisions.

The importance of problem solving agents in artificial intelligence lies in their ability to automate and streamline processes, improve efficiency, and reduce human error. These agents can also handle repetitive tasks, freeing up human resources for more complex and strategic work.

In addition, problem solving agents can learn and adapt from past experiences, making them even more effective over time. They can continuously analyze and optimize their problem-solving strategies, resulting in better decision-making and outcomes.

In conclusion, problem solving agents are a fundamental component of artificial intelligence. Their ability to analyze information, develop solutions, and make decisions has a significant impact on various industries and fields. Through their automation and optimization capabilities, problem solving agents contribute to improving efficiency, reducing errors, and enhancing decision-making processes.

Problem Solving Agent Architecture

A problem-solving agent is a central component in the field of artificial intelligence that is designed to tackle complex problems and find solutions. The architecture of a problem-solving agent consists of several key components that work together to achieve intelligent problem-solving.

One of the main components of a problem-solving agent is the knowledge base. This is where the agent stores relevant information and data that it can use to solve problems. The knowledge base can include facts, rules, and heuristics that the agent has acquired through learning or from experts in the domain.

Another important component of a problem-solving agent is the inference engine. This is the part of the agent that is responsible for reasoning and making logical deductions. The inference engine uses the knowledge base to generate possible solutions to a problem by applying various reasoning techniques, such as deduction, induction, and abduction.

Furthermore, a problem-solving agent often includes a search algorithm or strategy. This is used to systematically explore possible solutions and search for the best one. The search algorithm can be guided by various heuristics or constraints to efficiently navigate through the solution space.

In addition to these components, a problem-solving agent may also have a learning component. This allows the agent to improve its problem-solving capabilities over time through experience. The learning component can help the agent adapt its knowledge base, refine its inference engine, or adjust its search strategy based on feedback or new information.

Overall, the architecture of a problem-solving agent is designed to enable intelligent problem-solving by combining knowledge representation, reasoning, search, and learning. By utilizing these components, problem-solving agents can tackle a wide range of problems and find effective solutions in various domains.

Uninformed Search Algorithms

In the field of artificial intelligence, problem-solving agents are often designed to navigate a large search space in order to find a solution to a given problem. Uninformed search algorithms, also known as blind search algorithms, are a class of algorithms that do not use any additional information about the problem to guide their search.

Breadth-First Search (BFS)

Breadth-First Search (BFS) is one of the most basic uninformed search algorithms. It explores all the neighbor nodes at the present depth before moving on to the nodes at the next depth level. BFS is implemented using a queue data structure, where the nodes to be explored are added to the back of the queue and the nodes to be explored next are removed from the front of the queue.

For example, BFS can be used to find the shortest path between two cities on a road map, exploring all possible paths in a breadth-first manner to find the optimal solution.

Depth-First Search (DFS)

Depth-First Search (DFS) is another uninformed search algorithm that explores the deepest path first before backtracking. It is implemented using a stack data structure, where nodes are added to the top of the stack and the nodes to be explored next are removed from the top of the stack.

DFS can be used in situations where the goal state is likely to be far from the starting state, as it explores the deepest paths first. However, it may get stuck in an infinite loop if there is a cycle in the search space.

For example, DFS can be used to solve a maze, exploring different paths until the goal state (exit of the maze) is reached.

Overall, uninformed search algorithms provide a foundational approach to problem-solving in artificial intelligence. They do not rely on any additional problem-specific knowledge, making them applicable to a wide range of problems. While they may not always find the optimal solution or have high efficiency, they provide a starting point for more sophisticated search algorithms.

Breadth-First Search

Breadth-First Search is a problem-solving algorithm commonly used in artificial intelligence. It is an uninformed search algorithm that explores all the immediate variations of a problem before moving on to the next level of variations.

Examples of problems that can be solved using Breadth-First Search include finding the shortest path between two points in a graph, solving a sliding puzzle, or searching for a word in a large text document.

How Breadth-First Search Works

The Breadth-First Search algorithm starts at the initial state of the problem and expands all the immediate successor states. It then explores the successor states of the expanded states, continuing this process until a goal state is reached.

At each step of the algorithm, the breadth-first search maintains a queue of states to explore. The algorithm removes a state from the front of the queue, explores its successor states, and adds them to the back of the queue. This ensures that states are explored in the order they were added to the queue, resulting in a breadth-first exploration of the problem space.

The algorithm also keeps track of the visited states to avoid revisiting them in the future, preventing infinite loops in cases where the problem space contains cycles.

Benefits and Limitations

Breadth-First Search guarantees that the shortest path to a goal state is found, if such a path exists. It explores all possible paths of increasing lengths until a goal state is reached, ensuring that shorter paths are explored first.

However, the main limitation of Breadth-First Search is its memory requirements. As it explores all immediate successor states, it needs to keep track of a large number of states in memory. This can become impractical for problems with a large state space. Additionally, Breadth-First Search does not take into account the cost or quality of the paths it explores, making it less suitable for problems with complex cost or objective functions.

Depth-First Search

Depth-First Search (DFS) is a common algorithm used in the field of artificial intelligence to solve various types of problems. It is a search strategy that explores as far as possible along each branch of a tree-like structure before backtracking.

In the context of problem-solving agents, DFS is often used to traverse graph-based problem spaces in search of a solution. This algorithm starts at an initial state and explores all possible actions from that state until a goal state is found or all possible paths have been exhausted.

One example of using DFS in artificial intelligence is solving mazes. The agent starts at the entrance of the maze and explores one path at a time, prioritizing depth rather than breadth. It keeps track of the visited nodes and backtracks whenever it encounters a dead end, until it reaches the goal state (the exit of the maze).

Another example is solving puzzles, such as the famous Eight Queens Problem. In this problem, the agent needs to place eight queens on a chessboard in such a way that no two queens threaten each other. DFS can be used to explore all possible combinations of queen placements, backtracking whenever a placement is found to be invalid, until a valid solution is found or all possibilities have been exhausted.

DFS has advantages and disadvantages. Its main advantage is its simplicity and low memory usage, as it only needs to store the path from the initial state to the current state. However, it can get stuck in infinite loops if not implemented properly, and it may not always find the optimal solution.

In conclusion, DFS is a useful algorithm for problem-solving agents in artificial intelligence. It can be applied to a wide range of problems and provides a straightforward approach to exploring problem spaces. By understanding its strengths and limitations, developers can effectively utilize DFS to find solutions efficiently.

Iterative Deepening Depth-First Search

Iterative Deepening Depth-First Search (IDDFS) is a popular search algorithm used in problem solving within the field of artificial intelligence. It is a combination of depth-first search and breadth-first search algorithms and is designed to overcome some of the limitations of traditional depth-first search.

IDDFS operates in a similar way to depth-first search by exploring a problem space depth-wise. However, it does not keep track of the visited nodes in the search tree as depth-first search does. Instead, it uses a depth limit, which is gradually increased with each iteration, to restrict the depth to which it explores the search tree. This allows IDDFS to gradually explore the search space, starting from a shallow depth and progressively moving to deeper depths.

The iterative deepening depth-first search algorithm works by repeatedly performing depth-limited searches, incrementing the depth limit by one with each iteration. It performs a depth-first search to a given depth limit and if the goal state is not found, it increases the depth limit and performs the search again. This iterative process continues until the goal state is found or the entire search space has been explored.

IDDFS combines the advantages of both depth-first search and breadth-first search. It has the completeness of breadth-first search, meaning it is guaranteed to find a solution if one exists in the search space. At the same time, it preserves the memory efficiency of depth-first search by only keeping track of the current path being explored. This makes it an efficient algorithm for solving problems that have large or infinite search spaces.

Advantages of Iterative Deepening Depth-First Search

1. Completeness: IDDFS is a complete algorithm, meaning it is guaranteed to find a solution if one exists.

2. Memory efficiency: IDDFS only keeps track of the current path being explored, making it memory-efficient compared to breadth-first search which needs to store the entire search tree in memory.

Disadvantages of Iterative Deepening Depth-First Search

1. Redundant work: IDDFS performs multiple depth-limited searches, which can result in redundant work as nodes may be explored multiple times at different depths.

2. Inefficient for non-uniform branching factors: If the branching factor of the search tree varies greatly across different levels, IDDFS may spend a significant amount of time exploring deep levels with high branching factors, leading to inefficiency.

In conclusion, iterative deepening depth-first search is a powerful algorithm used in problem solving within artificial intelligence. It combines the efficiency of depth-first search with the completeness of breadth-first search, making it a valuable tool for solving problems that involve large or infinite search spaces.

Informed Search Algorithms

In artificial intelligence, problem-solving agents are designed to find solutions to complex problems by applying search algorithms. One class of search algorithms is known as informed search algorithms, which make use of additional knowledge or heuristics to guide the search process.

These algorithms are particularly useful when the problem space is large and the search process needs to be optimized. By using heuristics, informed search algorithms can prioritize certain paths or nodes that are more likely to lead to a solution.

Examples of Informed Search Algorithms

• A* algorithm: This is a widely used informed search algorithm that combines the benefits of both breadth-first search and best-first search approaches. It uses a heuristic function to estimate the cost from a given node to the goal state, and selects the path with the lowest estimated cost.
• Greedy Best-First Search: This algorithm uses a heuristic function to prioritize nodes based on their estimated distance to the goal. It always chooses the path that appears to be closest to the goal, without considering the overall cost of the path.
• IDA* algorithm: Short for Iterative Deepening A*, this algorithm is an optimization of the A* algorithm. It performs a depth-first search with an increasing maximum depth limit, guided by a heuristic function. This allows it to find the optimal solution with less memory usage.

These are just a few examples of the many informed search algorithms that exist in the field of artificial intelligence. Each algorithm has its own advantages and is suitable for different types of problems. By applying these algorithms, problem-solving agents can efficiently navigate through complex problem spaces and find optimal solutions.

Uniform-Cost Search

In the field of artificial intelligence, problem-solving agents are designed to find optimal solutions to given problems. One common approach is the use of search algorithms to explore the problem space and find the best path from an initial state to a goal state. Uniform-cost search is one such algorithm that is widely used in various problem-solving scenarios.

Uniform-cost search works by maintaining a priority queue of states, with the cost of reaching each state as the priority. The algorithm starts with an initial state and repeatedly selects the state with the lowest cost from the queue for expansion. It then generates all possible successors of the selected state and adds them to the queue with their respective costs. This process continues until the goal state is reached or the queue is empty.

To illustrate the use of uniform-cost search, let’s consider an example of finding the shortest path from one city to another on a map. The map can be represented as a graph, with cities as the nodes and roads as the edges. Each road has a cost associated with it, representing the distance between the two cities it connects.

Using uniform-cost search, the algorithm would start from the initial city and explore the neighboring cities, considering the cost of each road. It would then continue expanding the cities with the lowest cumulative costs, gradually moving towards the goal city. The algorithm terminates when it reaches the goal city or exhausts all possible paths.

Uniform-cost search is particularly useful in scenarios where the goal is to find the optimal solution with the lowest cost. It guarantees the discovery of the optimal path by exploring all possible paths in a systematic way. However, it can be computationally expensive in terms of time and memory requirements, especially in large problem spaces.

In conclusion, uniform-cost search is an effective algorithm used by problem-solving agents in artificial intelligence to find optimal solutions. It systematically explores all possible paths, guaranteeing the discovery of the optimal solution. However, it can be computationally expensive and requires significant memory usage, making it less suitable for problems with large or infinite state spaces.

Greedy Best-First Search

Greedy Best-First Search (GBFS) is a problem-solving algorithm used in artificial intelligence. It is an example of an intelligent agent that aims to find the most promising solution based solely on its heuristic function.

The GBFS algorithm starts by initializing the initial state of the problem. Then, it evaluates all the neighboring states using a heuristic function, which estimates the cost or value of each state based on certain criteria. The algorithm selects the state that has the lowest heuristic value as the next state to explore.

This means that GBFS always chooses the path that seems most promising at the current moment, without considering the global picture or evaluating future consequences. It follows a greedy approach by making locally optimal decisions. This can sometimes lead to suboptimal solutions if the initial path chosen ends up being a dead-end or if there is a better path further down the line.

GBFS can be used in various problem-solving scenarios. One example is the traveling salesman problem, where the goal is to find the shortest possible route that visits a set of cities and returns to the starting point. The algorithm can evaluate the heuristic value of each potential next city based on its proximity to the current city and select the city with the shortest distance as the next destination.

Another example is the maze-solving problem, where GBFS can be used to navigate through a maze by evaluating the heuristic value of each possible move, such as the distance to the exit or the number of obstacles in the path. The algorithm then chooses the move that leads to the most promising outcome based on the heuristic evaluation.

Overall, GBFS is an example of an intelligent agent in artificial intelligence that utilizes a heuristic function to make locally optimal decisions in problem-solving scenarios. While it may not always guarantee the optimal solution, it can often provide a good approximation and is efficient in many practical applications.

A* search is a widely used algorithm in artificial intelligence for problem-solving. It is an informed search algorithm that combines the features of uniform-cost search with heuristic functions to find an optimal path from a start state to a goal state.

The A* search algorithm is especially useful when dealing with problems that have a large search space or multiple possible paths to the goal state. It uses a heuristic function to estimate the cost of reaching the goal from each state and adds this estimated cost to the actual cost of getting to that state so far. The algorithm then explores the states with the lowest total cost first, making it a best-first search algorithm.

How A* Search Works

At each step of the A* search algorithm, it selects the state with the lowest total cost from the open set of states to explore next. The total cost is calculated as the sum of the actual cost of reaching the state plus the estimated cost of reaching the goal from that state. The open set is initially populated with the start state, and the algorithm continues until the goal state is reached or the open set is empty.

To estimate the cost of reaching the goal, A* search uses a heuristic function, often denoted as h(n), which provides an optimistic estimate of the cost from a given state to the goal. This heuristic function is problem-specific and can be defined based on various factors, such as distance, time, or other relevant considerations.

One commonly used heuristic function is the Manhattan distance, which calculates the distance between two points in a grid-like environment by summing the absolute differences of their x and y coordinates. Another example is the Euclidean distance, which calculates the straight-line distance between two points in a continuous space.

Examples of A* Search

A* search has been successfully applied to various problem-solving scenarios. Some examples include:

• Pathfinding in a grid-based environment, such as finding the shortest path in a maze or a game level.
• Optimal route planning for vehicles or delivery services, considering factors like traffic conditions or fuel consumption.
• Puzzle solving, such as finding the minimum number of moves to solve a sliding puzzle or the Tower of Hanoi problem.
• Scheduling and resource allocation, where the objective is to minimize costs or maximize efficiency.

These examples demonstrate the versatility and effectiveness of A* search in solving a wide range of problems in artificial intelligence.

Constraint Satisfaction Problems

In the field of artificial intelligence, constraint satisfaction problems (CSPs) are a type of problem-solving agent that deals with a set of variables and a set of constraints that define the relationships between those variables. The aim is to find an assignment of values to the variables that satisfies all the given constraints.

One example of a CSP is the Sudoku puzzle. In this puzzle, the variables are the empty cells, and the constraints are that each row, column, and 3×3 subgrid must contain distinct numbers from 1 to 9. The problem-solving agent must find a valid assignment of numbers to the variables in order to solve the puzzle.

Another example of a CSP is the map coloring problem. In this problem, the variables are the regions on a map, and the constraints are that adjacent regions cannot have the same color. The problem-solving agent must assign a color to each region in such a way that no adjacent regions have the same color.

CSPs can be solved using various algorithms, such as backtracking, constraint propagation, and local search. These algorithms iteratively explore the search space of possible variable assignments, while taking into account the constraints, in order to find a valid solution.

Overall, constraint satisfaction problems provide a framework for modeling and solving a wide range of problems in artificial intelligence, from puzzles to planning and scheduling problems. By representing the problem as a set of variables and constraints, problem-solving agents can efficiently search for solutions that satisfy all the given constraints.

Backtracking

Backtracking is a common technique used in solving problems in artificial intelligence. It is particularly useful when exploring all possible solutions to a problem. Backtracking involves a systematic approach to finding a solution by incrementally building a potential solution, and when a dead-end is encountered, it backtracks and tries a different path.

One example of backtracking is the n-queens problem . In this problem, the goal is to place n queens on an n x n chessboard such that no two queens can attack each other. Backtracking can be used to find all possible solutions to this problem by systematically placing queens on the board and checking if the current configuration is valid. If a configuration is not valid, the algorithm backtracks and tries a different position.

Another example of backtracking is the knight’s tour problem . In this problem, the goal is to find a sequence of moves for a knight on a chessboard such that it visits every square exactly once. Backtracking can be used to explore all possible paths the knight can take, and when a dead-end is encountered, it backtracks and tries a different path.

Backtracking algorithms can be time-consuming as they may need to explore a large number of potential solutions. However, they are powerful and flexible, making them suitable for solving a wide range of problems. In artificial intelligence, backtracking is often used in problem-solving agents to find optimal solutions or to explore the space of possible solutions.

Forward Checking

Forward Checking is a technique used by problem-solving agents in artificial intelligence to improve the efficiency and effectiveness of their search algorithms. It is particularly useful when dealing with constraint satisfaction problems, where there are variables that need to be assigned values while satisfying certain constraints.

How does it work?

When a variable is assigned a value, forward checking updates the remaining domains of the variables by removing any values that are inconsistent with the assigned value, based on the constraints. This helps reduce the search space and allows the agent to explore more promising paths towards a solution.

For example, let’s consider a Sudoku puzzle, which is a classic constraint satisfaction problem. The goal is to fill a 9×9 grid with digits from 1 to 9, such that each row, each column, and each of the nine 3×3 subgrids contains all of the digits from 1 to 9 without repetition.

When forward checking is applied to solve a Sudoku puzzle, the agent starts by assigning a value to an empty cell. Then, it updates the domains of the remaining variables (empty cells) by removing any values that violate the Sudoku constraints. This reduces the number of possible values for the remaining variables and improves the efficiency of the search algorithm.

Advantages of Forward Checking

Forward checking has several advantages when used by problem-solving agents:

• It helps reduce the search space by eliminating values that are inconsistent with the constraints.
• It can lead to more efficient search algorithms by guiding the agent towards more promising paths.
• It can improve the accuracy of the search algorithm by considering the constraints during the assignment of values.

Overall, forward checking is an important technique used by problem-solving agents to efficiently solve constraint satisfaction problems, such as Sudoku puzzles, and improve the effectiveness of their search algorithms.

Arc Consistency

Arc consistency is a key concept in artificial intelligence problem-solving agents, specifically in constraint satisfaction problems (CSPs). CSPs are mathematical problems that involve finding a solution that satisfies a set of constraints.

In a CSP, variables are assigned values from a domain, and constraints define the relationships between the variables. Arc consistency is a technique used to reduce the search space by ensuring that all values in the domain are consistent with the constraints.

For example, consider a scheduling problem where we need to assign tasks to workers. We have a set of constraints that specify which tasks can be assigned to which workers. Arc consistency would involve checking each constraint to ensure that the assigned values satisfy the constraints. If a constraint is not satisfied, the agent would backtrack and try a different assignment.

The arc consistency technique uses a process called domain filtering, which iteratively eliminates values from the domain that are not consistent with the current assignments and constraints. This process continues until no more values can be removed or until a solution is found.

In this example, initially both Task 1 and Task 2 can be assigned to both Worker A and Worker B. However, by applying arc consistency, we can eliminate the assignments that violate the constraints. After applying arc consistency, we end up with the following assignments:

By applying arc consistency, we have reduced the solution space and ensured that all assignments satisfy the constraints. This allows the problem-solving agent to search for a solution more efficiently.

Game Playing Agents

Game playing agents are artificial intelligence agents that are designed to play games. These agents are capable of making decisions and taking actions in order to achieve the goal of winning the game. They use various problem solving techniques and strategies to analyze the current state of the game and make the best possible move.

There are several examples of game playing agents in artificial intelligence:

Game playing agents have been a subject of research and development in artificial intelligence for many years. They have contributed to advancements in areas such as machine learning, pattern recognition, and decision-making algorithms.

Minimax Algorithm

The Minimax Algorithm is a common solving approach used by intelligent agents in the field of artificial intelligence. It is primarily used in scenarios where an agent needs to make decisions in a competitive setting with an opponent.

The goal of the Minimax Algorithm is to determine the best possible move for an agent, assuming that the opponent is also playing optimally. It works by exploring all potential moves and their resulting outcomes, ultimately selecting the move that minimizes the maximum possible outcome for the opponent.

One example of the Minimax Algorithm in action is in the game of Chess. The agent (player) evaluates the potential moves it can make and computes the possible moves the opponent (opponent player) can make in response. The agent then simulates each possible sequence of moves, looking several moves ahead, and assigns a score to each sequence based on the predicted outcome. The agent selects the move that leads to the sequence with the lowest score, assuming the opponent will always make the move that maximizes their score.

Another example is in the game of Tic Tac Toe. The agent and the opponent each take turns making moves on a 3×3 grid. The agent uses the Minimax Algorithm to explore the possible outcomes of each move and selects the move that minimizes the maximum potential outcome for the opponent.

The Minimax Algorithm is a powerful tool for solving problems in artificial intelligence, as it allows intelligent agents to make optimal decisions in competitive settings. It can be applied to a wide range of scenarios beyond games, including decision-making processes in robotics, resource allocation, and strategic planning.

Alpha-Beta Pruning

In the field of artificial intelligence, one of the key techniques used by problem-solving agents is called alpha-beta pruning. This technique is employed in game playing algorithms, where the agent needs to make decisions that maximize its chances of winning.

The goal of alpha-beta pruning is to reduce the number of nodes that need to be evaluated in a game tree, without compromising the correctness of the agent’s decision. By pruning branches of the tree that are deemed to be less promising, the agent can save significant computational resources and make faster decisions.

How Alpha-Beta Pruning Works

Alpha-beta pruning is based on the concept of minimax algorithm, which explores the entire game tree to find the optimal move for the agent. However, unlike minimax, alpha-beta pruning stops exploring certain branches when it is determined that they will not affect the final decision.

The algorithm maintains two values called alpha and beta, which represent the best values achievable for the maximizing player and the minimizing player, respectively. As the agent explores the tree, it updates these values based on the current position and the possible moves.

If the agent finds a move that yields a value greater than or equal to the beta value, it means that the minimizing player can force a value greater than or equal to beta, so there is no need to explore that branch further. Similarly, if the agent finds a move that yields a value less than or equal to the alpha value, it means that the maximizing player can force a value less than or equal to alpha, so there is no need to explore that branch further either.

Benefits of Alpha-Beta Pruning

Alpha-beta pruning is a powerful technique that can greatly improve the efficiency of problem-solving agents in artificial intelligence. By avoiding the evaluation of unnecessary nodes in the game tree, agents can make faster decisions without sacrificing accuracy.

This technique is particularly useful in games with large branching factors, where the game tree can be extremely large. Alpha-beta pruning allows agents to focus their computational resources on the most promising branches, leading to more effective decision-making and improved gameplay.

Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a popular algorithm used in solving complex problems by artificial intelligence agents. It is particularly effective in problem domains with large state spaces and difficult decision-making processes.

MCTS simulates the problem-solving process by traversing a tree of possible actions and outcomes. It uses random sampling, or “Monte Carlo” simulations, to estimate the potential value or utility of each action. This allows the agent to focus its search on promising actions and avoid wasting time exploring unpromising ones.

The MCTS algorithm consists of four main steps: selection, expansion, simulation, and backpropagation. In the selection step, the algorithm chooses a node from the tree based on a selection policy, typically the Upper Confidence Bound (UCB). The expansion step adds child nodes to the selected node, representing possible actions. The simulation step performs a Monte Carlo simulation by randomly selecting actions and obtaining a simulated outcome. Finally, the backpropagation step updates the values of the nodes in the tree based on the simulation results.

By iteratively performing these steps, MCTS gradually builds up knowledge about the problem domain and improves its decision-making capabilities. It can be used in a wide range of problem-solving scenarios, such as playing board games, optimizing resource allocation, or finding optimal strategies in complex environments.

Overall, Monte Carlo Tree Search is an effective algorithm for solving problems in artificial intelligence. Its ability to balance exploration and exploitation allows agents to efficiently search large state spaces and find optimal solutions to complex problems.

Expert Systems

Expert systems are a type of problem-solving agents in the field of artificial intelligence. They are designed to mimic the behavior and knowledge of human experts in a specific domain. These systems use a combination of rules, inference engines, and knowledge bases to solve complex problems and provide expert-level solutions.

Expert systems can be found in various industries and domains, including healthcare, finance, manufacturing, and customer support. They are used to assist professionals in making complex decisions, troubleshoot problems, and provide expert advice.

One example of an expert system is IBM Watson, which gained fame for its victory on the television quiz show Jeopardy! Watson is designed to understand natural language, process large amounts of data, and provide accurate answers to questions. It utilizes machine learning techniques to improve its performance over time.

Another example is Dendral, an expert system developed in the 1960s to solve problems in organic chemistry. Dendral was able to analyze mass spectrometry data and identify the structure of organic compounds. It was one of the first successful applications of expert systems in the field of chemistry.

Expert systems can be classified as rule-based systems, where a set of rules is defined to guide the decision-making process. These rules are usually created by domain experts and encoded in the knowledge base of the system. The inference engine then uses these rules to reason and make inferences.

Overall, expert systems play a crucial role in artificial intelligence by combining human expertise and machine learning techniques to solve complex problems in various domains. They provide valuable insights and solutions, making them powerful tools for professionals in different industries.

Rule-Based Systems

Rule-based systems are a common type of problem-solving agent in artificial intelligence. These systems use a set of rules or “if-then” statements to solve problems. Each rule consists of a condition and an action. If the condition is met, then the action is performed.

Example 1: Expert Systems

One example of a rule-based system is an expert system. Expert systems are designed to mimic the decision-making abilities of human experts in a specific domain. They use a knowledge base of rules to provide advice or make decisions. For example, a medical expert system could use rules to diagnose a patient’s symptoms and recommend a course of treatment.

Example 2: Production Systems

Another example of a rule-based system is a production system. Production systems are commonly used in manufacturing and planning domains. They consist of rules that describe the steps to be taken in a production process. For example, a production system for building a car could have rules for assembling different components in a specific order.

In conclusion, rule-based systems are a powerful tool in artificial intelligence for solving problems. They use a set of rules to make decisions or perform actions based on specific conditions. Examples include expert systems and production systems.

Fuzzy Logic

Fuzzy logic is a branch of artificial intelligence that deals with reasoning that is approximate rather than precise. In contrast to traditional logic, which is based on binary true/false values, fuzzy logic allows for degrees of truth. This makes it particularly useful for problem solving agents in artificial intelligence, as it enables them to work with uncertain or ambiguous information.

One of the key advantages of fuzzy logic is its ability to handle imprecise data and make decisions based on incomplete or uncertain information. This makes it well-suited for applications such as decision-making systems, control systems, and expert systems.

One example of fuzzy logic in action is in weather forecasting. Since weather conditions can be difficult to predict with complete accuracy, fuzzy logic can be used to analyze various factors such as temperature, humidity, and wind speed, and make a determination about the likelihood of rain or sunshine.

Another example is in autonomous vehicles. Fuzzy logic can be used to interpret sensor data, such as distance, speed, and road conditions, and make decisions about how to navigate and respond to the environment. This allows the vehicle to adapt and make intelligent decisions in real-time.

Bayesian Networks

Bayesian Networks are a powerful tool in the field of Artificial Intelligence, used by problem-solving agents to model uncertain knowledge and make decisions based on probability.

Bayesian Networks are graphical models that represent a set of variables and their probabilistic relationships through a directed acyclic graph. The nodes in the graph represent the variables, while the edges represent the dependencies between the variables.

These networks are widely used in various domains, including healthcare, finance, and robotics, to name a few. They are particularly useful when dealing with uncertain and complex situations, where decisions need to be made based on incomplete or imperfect information.

Examples of Bayesian Networks:

• Medical Diagnosis: Bayesian Networks can be used to model and diagnose diseases based on symptoms, medical history, and test results. The network can update the probabilities of different diseases based on new evidence and help in making accurate diagnoses.
• Weather Prediction: Bayesian Networks can be used to model the relationships between different weather variables such as temperature, humidity, and wind speed. By updating the probabilities of these variables based on observed data, the network can predict the likelihood of different weather conditions.

In both examples, Bayesian Networks provide a systematic framework for combining prior knowledge with observed evidence to make informed decisions. They enable problem-solving agents to reason under uncertainty and update beliefs in a principled and consistent manner.

Machine Learning Agents

Machine learning agents are a subset of artificial intelligence agents that utilize machine learning algorithms to solve problems. These agents are capable of learning from experience and improving their performance over time. They are trained on large datasets and use various techniques to analyze and interpret the data, such as deep learning and reinforcement learning.

One example of a machine learning agent is a predictive model that is trained to predict future outcomes based on historical data. For example, in finance, machine learning agents can be used to predict stock prices or identify patterns in market data to make informed investment decisions.

Another example of a machine learning agent is a virtual assistant, such as Siri or Alexa, that uses natural language processing and machine learning techniques to understand and respond to user queries and commands. These virtual assistants continuously learn from user interactions and improve their accuracy in interpreting and responding to user inputs.

Machine learning agents have revolutionized many industries and have the potential to drive innovation and improve efficiency in various domains. By leveraging the power of data and advanced algorithms, these agents can solve complex problems and make intelligent decisions that were previously not possible.

Reinforcement Learning Agents

Reinforcement learning agents are a type of problem-solving agent in artificial intelligence. These agents are designed to learn and improve their behavior through trial and error, using a system of rewards and punishments.

One example of a reinforcement learning agent is an autonomous robot that learns to navigate its environment. The robot starts with no prior knowledge of the environment and must explore and interact with its surroundings to learn how to reach a specific goal. It receives positive reinforcement, such as a reward, when it successfully performs the desired action, and negative reinforcement, such as a punishment or penalty, when it makes a mistake.

Another example of a reinforcement learning agent is a computer program that learns to play a game. The program is initially unaware of the rules and strategies of the game and must learn through repeated play. It receives positive reinforcement when it makes a winning move or achieves a high score, and negative reinforcement when it makes a losing move or receives a low score. Over time, the program learns to make better decisions and improve its performance.

Reinforcement Learning Process

The reinforcement learning process consists of the following steps:

• Observation: The agent observes the current state of the environment.
• Action: The agent selects an action to perform based on its current knowledge and strategy.
• Reward: The agent receives a reward or punishment based on the outcome of its action.
• Learning: The agent adjusts its strategy and behavior based on the received reward or punishment.
• Iteration: The process is repeated, with the agent continuously learning and improving over time.

Applications of Reinforcement Learning Agents

Reinforcement learning agents have various applications in artificial intelligence, including:

• Autonomous robotics
• Game playing
• Optimization problems
• Resource allocation
• Financial trading

These examples demonstrate how reinforcement learning agents can adapt and improve their behavior in different environments and problem-solving scenarios.

Genetic Algorithms

Genetic Algorithms are a type of problem-solving technique used in artificial intelligence. They are inspired by the process of natural selection and genetic inheritance in living organisms. These algorithms use a population of possible solutions to a problem and apply genetic operators such as selection, crossover, and mutation to evolve and improve the solutions over time.

Genetic Algorithms have been successfully applied to various optimization problems, such as finding the best combination of parameters for a machine learning model or optimizing the routing of vehicles in logistics. They are particularly useful in problems where there is no deterministic algorithm to find an optimal solution.

Here are a few examples of how Genetic Algorithms can be used:

In each of these examples, Genetic Algorithms can be used to search the solution space more efficiently and find near-optimal or optimal solutions. The population-based approach of Genetic Algorithms allows for exploration of multiple potential solutions simultaneously, increasing the chances of finding a good solution.

Overall, Genetic Algorithms are a powerful and flexible problem-solving technique in the field of artificial intelligence. They can be applied to a wide range of problems and have been proven to be effective in finding optimal or near-optimal solutions.

Swarm Intelligence

Swarm intelligence is a field of artificial intelligence that involves studying the collective behavior of multi-agent systems in order to solve complex problems. In this approach, individual agents work together as a swarm to find optimal solutions without centralized control or coordination.

Central to the concept of swarm intelligence is the idea that intelligence emerges from the interactions and cooperation of simple agents. These agents, often inspired by natural systems such as ant colonies or bird flocks, follow simple rules and communicate with each other to achieve a common goal.

Applications

• Swarm intelligence has been used in various problem-solving scenarios, including optimization problems, task allocation, and decision-making.
• One notable application is in robotics, where swarms of robots can collectively explore and map unknown environments, perform search and rescue operations, or even assemble complex structures.
• Another application is in finance, where swarm intelligence algorithms are used to analyze and predict stock market trends or optimize investment portfolios.
• One of the main advantages of swarm intelligence is its robustness and adaptability. As individual agents can communicate and adjust their behavior based on the information from their neighbors, the swarm as a whole can quickly adapt to changes or disturbances in the environment.
• Swarm intelligence also offers a scalable solution, as the performance of the swarm can improve with the addition of more agents.
• Furthermore, swarm intelligence algorithms are often computationally efficient and can handle large-scale problems that would be intractable for traditional optimization techniques.

In conclusion, swarm intelligence is a promising approach in artificial intelligence that leverages the collective intelligence of simple agents to solve complex problems. Its applications span various domains, and its advantages make it an appealing technique for solving real-world challenges.

Questions and answers

What are problem solving agents in artificial intelligence.

Problem solving agents in artificial intelligence are intelligent systems that are designed to solve complex problems by searching for the best solution based on well-defined rules and goals.

How do problem solving agents work?

Problem solving agents work by analyzing a given problem, breaking it into smaller sub-problems, and then searching for a solution by applying various problem-solving techniques, such as heuristics, pattern recognition, logical reasoning, and machine learning algorithms.

Can you give an example of a problem solving agent?

One example of a problem solving agent is a chess-playing computer program. It analyzes the current state of the chessboard, generates possible moves, evaluates their outcomes using a specified evaluation function, and then selects the move with the highest expected outcome as the solution to the problem of finding the best move.

What are some other applications of problem solving agents?

Problem solving agents have a wide range of applications in various fields. They are used in robotics to plan and execute actions, in automated planning systems to optimize resource allocation, in natural language processing to interpret and respond to user queries, and in medical diagnosis to analyze symptoms and suggest possible treatments.

Are problem solving agents capable of solving all types of problems?

No, problem solving agents are not capable of solving all types of problems. Their effectiveness depends on the specific problem domain and the availability of knowledge and resources. Some problems may be too complex or ill-defined, making it difficult for problem solving agents to find optimal solutions.

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[MCQ’s] Artificial Intelligence

Introduction to intelligent systems and intelligent agents, search techniques, knowledge and reasoning, uncertain knowledge and reasoning, natural language processing.

1. What is the main task of a problem-solving agent? A. Solve the given problem and reach to goal B. To find out which sequence of action will get it to the goal state C. Both A and B D. None of the Above Ans : C Explanation: The problem-solving agents are one of the goal-based agents

2. What is Initial state + Goal state in Search Terminology? A. Problem Space B. Problem Instance C. Problem Space Graph D. Admissibility Ans : B Explanation: Problem Instance : It is Initial state + Goal state.

3. What is Time Complexity of Breadth First search algorithm? A. b B. b^d C. b^2 D. b^b Ans : B Explanation: Time Complexity of Breadth First search algorithm is b^d.

4. Depth-First Search is implemented in recursion with _______ data structure. A. LIFO B. LILO C. FIFO D. FILO Ans : A Explanation: Depth-First Search implemented in recursion with LIFO stack data structure.

5. How many types are available in uninformed search method? A. 2 B. 3 C. 4 D. 5 Ans : D Explanation: The five types of uninformed search method are Breadth-first, Uniform-cost, Depth-first, Depth-limited and Bidirectional search.

6. Which data structure conveniently used to implement BFS? A. Stacks B. Queues C. Priority Queues D. None of the Above Ans : B Explanation: Queue is the most convenient data structure, but memory used to store nodes can be reduced by using circular queues.

7. How many types of informed search method are in artificial intelligence? A. 2 B. 3 C. 4 D. 5 Ans : C Explanation: The four types of informed search method are best-first search, Greedy best-first search, A* search and memory bounded heuristic search.

8. Greedy search strategy chooses the node for expansion in ___________ A. Shallowest B. Deepest C. The one closest to the goal node D. Minimum heuristic cost Ans : C Explanation: Sometimes minimum heuristics can be used, sometimes maximum heuristics function can be used. It depends upon the application on which the algorithm is applied.

9. What is disadvantage of Greedy Best First Search? A. This algorithm is neither complete, nor optimal. B. It can get stuck in loops. It is not optimal. C. There can be multiple long paths with the cost ≤ C* D. may not terminate and go on infinitely on one path Ans : B Explanation: The disadvantage of Greedy Best First Search is that it can get stuck in loops. It is not optimal.

10. Searching using query on Internet is, use of ___________ type of agent. A. Offline agent B. Online Agent C. Goal Based D. Both B and C Ans : D Explanation: Refer to the definitions of both the type of agent.

11. An AI system is composed of? A. agent B. environment C. Both A and B D. None of the Above Ans : C Explanation: An AI system is composed of an agent and its environment.

12. Which instruments are used for perceiving and acting upon the environment? A. Sensors and Actuators B. Sensors C. Perceiver D. Perceiver and Sensor Ans : A Explanation: An agent is anything that can be viewed as perceiving and acting upon the environment through the sensors and actuators.

13. Which of the following is not a type of agents in artificial intelligence? A. Model based B. Utility based C. Simple reflex D. target based Ans : D Explanation: The four types of agents are Simple reflex, Model based, Goal based and Utility based agents.

14. Which is used to improve the agents performance? A. Perceiving B. Observing C. Learning D. Sequence Ans : C Explanation: An agent can improve its performance by storing its previous actions.

15. Rationality of an agent does not depends on? A. performance measures B. Percept Sequence C. reaction D. actions Ans : C Explanation: Rationality of an agent does not depends on reaction

16. Agent’s structure can be viewed as ? A. Architecture B. Agent Program C. Architecture + Agent Program D. None of the Above Ans : C Explanation: Agent’s structure can be viewed as – Agent = Architecture + Agent Program

17. What is the action of task environment in artificial intelligence? A. Problem B. Solution C. Agent D. Observation Ans : A Explanation: Task environments will pose a problem and rational agent will find the solution for the posed problem.

18. What kind of environment is crossword puzzle? A. Dynamic B. Static C. Semi Dynamic D. Continuous Ans : B Explanation: As the problem in crossword puzzle are posed at beginning itself, So it is static.

19. What could possibly be the environment of a Satellite Image Analysis System? A. Computers in space and earth B. Image categorization techniques C. Statistical data on image pixel intensity value and histograms D. All of the above Ans : D Explanation: An environment is something which agent stays in.

20. Which kind of agent architecture should an agent an use? A. Relaxed B. Relational C. Both A and B D. None of the AboveAns : C Explanation: Because an agent may experience any kind of situation, So that an agent should use all kinds of architecture.

21. Which depends on the percepts and actions available to the agent? a) Agent b) Sensor c) Design problem d) None of the mentioned Answer: c Explanation: The design problem depends on the percepts and actions available to the agent, the goals that the agent’s behavior should satisfy.

22. Which were built in such a way that humans had to supply the inputs and interpret the outputs? a) Agents b) AI system c) Sensor d) Actuators Answer: b Explanation: AI systems were built in such a way that humans had to supply the inputs and interpret the outputs.

23. Which technology uses miniaturized accelerometers and gyroscopes? a) Sensors b) Actuators c) MEMS d) None of the mentioned Answer: c Explanation: Micro ElectroMechanical System uses miniaturized accelerometers and gyroscopes and is used to produce actuators.

24. What is used for tracking uncertain events? a) Filtering algorithm b) Sensors c) Actuators d) None of the mentioned Answer: a Explanation: Filtering algorithm is used for tracking uncertain events because in this the real perception is involved.

25. What is not represented by using propositional logic? a) Objects b) Relations c) Both Objects & Relations d) None of the mentioned Answer: c Explanation: Objects and relations are not represented by using propositional logic explicitly.

26. Which functions are used as preferences over state history? a) Award b) Reward c) Explicit d) Implicit Answer: b Explanation: Reward functions may be that preferences over states are really compared from preferences over state histories.

27. Which kind of agent architecture should an agent an use? a) Relaxed b) Logic c) Relational d) All of the mentioned Answer: d Explanation: Because an agent may experience any kind of situation, So that an agent should use all kinds of architecture.

28. Specify the agent architecture name that is used to capture all kinds of actions. a) Complex b) Relational c) Hybrid d) None of the mentioned Answer: c Explanation: A complete agent must be able to do anything by using hybrid architecture.

29. Which agent enables the deliberation about the computational entities and actions? a) Hybrid b) Reflective c) Relational d) None of the mentioned Answer: b Explanation: Because it enables the agent to capture within itself.

30. What can operate over the joint state space? a) Decision-making algorithm b) Learning algorithm c) Complex algorithm d) Both Decision-making & Learning algorithm

31. What is the action of task environment in artificial intelligence? a) Problem b) Solution c) Agent d) Observation Answer: a Explanation: Task environments will pose a problem and rational agent will find the solution for the posed problem.

32. What is the expansion if PEAS in task environment? a) Peer, Environment, Actuators, Sense b) Perceiving, Environment, Actuators, Sensors c) Performance, Environment, Actuators, Sensors d) None of the mentioned Answer: c Explanation: Task environment will contain PEAS which is used to perform the action independently.

33. What kind of observing environments are present in artificial intelligence? a) Partial b) Fully c) Learning d) Both Partial & Fully Answer: d Explanation: Partial and fully observable environments are present in artificial intelligence.

34. What kind of environment is strategic in artificial intelligence? a) Deterministic b) Rational c) Partial d) Stochastic Answer: a Explanation: If the environment is deterministic except for the action of other agent is called deterministic.

35. What kind of environment is crossword puzzle? a) Static b) Dynamic c) Semi Dynamic d) None of the mentioned Answer: a Explanation: As the problem in crossword puzzle are posed at beginning itself, So it is static.

36. What kind of behavior does the stochastic environment posses? a) Local b) Deterministic c) Rational d) Primary Answer: a Explanation: Stochastic behavior are rational because it avoids the pitfall of predictability.

37. Which is used to select the particular environment to run the agent? a) Environment creator b) Environment Generator c) Both Environment creator & Generator d) None of the mentioned Answer: b Explanation: None.

38. Which environment is called as semi dynamic? a) Environment does not change with the passage of time b) Agent performance changes c) Environment will be changed d) Environment does not change with the passage of time, but Agent performance changes Answer: d Explanation: If the environment does not change with the passage of time, but the agent performance changes by time.

39. Where does the performance measure is included? a) Rational agent b) Task environment c) Actuators d) Sensor Answer: b Explanation: In PEAS, Where P stands for performance measure which is always included in task environment.

1. Which search strategy is also called as blind search? a) Uninformed search b) Informed search c) Simple reflex search d) All of the mentioned Answer: a Explanation: In blind search, We can search the states without having any additional information. So uninformed search method is blind search.

2. How many types are available in uninformed search method? a) 3 b) 4 c) 5 d) 6 Answer: c Explanation: The five types of uninformed search method are Breadth-first, Uniform-cost, Depth-first, Depth-limited and Bidirectional search.

3. Which search is implemented with an empty first-in-first-out queue? a) Depth-first search b) Breadth-first search c) Bidirectional search d) None of the mentioned Answer: b Explanation: Because of FIFO queue, it will assure that the nodes that are visited first will be expanded first.

4. When is breadth-first search is optimal? a) When there is less number of nodes b) When all step costs are equal c) When all step costs are unequal d) None of the mentioned Answer: b Explanation: Because it always expands the shallowest unexpanded node.

5. How many successors are generated in backtracking search? a) 1 b) 2 c) 3 d) 4 Answer: a Explanation: Each partially expanded node remembers which successor to generate next because of these conditions, it uses less memory.

6. What is the space complexity of Depth-first search? a) O(b) b) O(bl) c) O(m) d) O(bm) Answer: d Explanation: O(bm) is the space complexity where b is the branching factor and m is the maximum depth of the search tree.

7. How many parts does a problem consists of? a) 1 b) 2 c) 3 d) 4 Answer: d Explanation: The four parts of the problem are initial state, set of actions, goal test and path cost.

8. Which algorithm is used to solve any kind of problem? a) Breadth-first algorithm b) Tree algorithm c) Bidirectional search algorithm d) None of the mentioned Answer: b Explanation: Tree algorithm is used because specific variants of the algorithm embed different strategies.

9. Which search algorithm imposes a fixed depth limit on nodes? a) Depth-limited search b) Depth-first search c) Iterative deepening search d) Bidirectional search Answer: a Explanation: None.

10. Which search implements stack operation for searching the states? a) Depth-limited search b) Depth-first search c) Breadth-first search d) None of the mentioned Answer: b

11. What is the other name of informed search strategy? a) Simple search b) Heuristic search c) Online search d) None of the mentioned Answer: b Explanation: A key point of informed search strategy is heuristic function, So it is called as heuristic function.

12. How many types of informed search method are in artificial intelligence? a) 1 b) 2 c) 3 d) 4 Answer: d Explanation: The four types of informed search method are best-first search, Greedy best-first search, A* search and memory bounded heuristic search.

13. Which search uses the problem specific knowledge beyond the definition of the problem? a) Informed search b) Depth-first search c) Breadth-first search d) Uninformed search Answer: a Explanation: Informed search can solve the problem beyond the function definition, So does it can find the solution more efficiently.

14. Which function will select the lowest expansion node at first for evaluation? a) Greedy best-first search b) Best-first search c) Depth-first search d) None of the mentioned Answer: b Explanation: The lowest expansion node is selected because the evaluation measures distance to the goal.

15. What is the heuristic function of greedy best-first search? a) f(n) != h(n) b) f(n) < h(n) c) f(n) = h(n) d) f(n) > h(n) Answer: c Explanation: None.

16. Which search uses only the linear space for searching? a) Best-first search b) Recursive best-first search c) Depth-first search d) None of the mentioned Answer: b Explanation: Recursive best-first search will mimic the operation of standard best-first search, but using only the linear space.

17. Which method is used to search better by learning? a) Best-first search b) Depth-first search c) Metalevel state space d) None of the mentioned Answer: c Explanation: This search strategy will help to problem solving efficiency by using learning.

18. Which search is complete and optimal when h(n) is consistent? a) Best-first search b) Depth-first search c) Both Best-first & Depth-first search d) A* search Answer: d Explanation: None.

19. Which is used to improve the performance of heuristic search? a) Quality of nodes b) Quality of heuristic function c) Simple form of nodes d) None of the mentioned Answer: b Explanation: Good heuristic can be constructed by relaxing the problem, So the performance of heuristic search can be improved.

20. Which search method will expand the node that is closest to the goal? a) Best-first search b) Greedy best-first search c) A* search d) None of the mentioned Answer: b Explanation: Because of using greedy best-first search, It will quickly lead to the solution of the problem.

21. In many problems the path to goal is irrelevant, this class of problems can be solved using ____________ a) Informed Search Techniques b) Uninformed Search Techniques c) Local Search Techniques d) Informed & Uninformed Search Techniques Answer: c Explanation: If the path to the goal does not matter, we might consider a different class of algorithms, ones that do not worry about paths at all. Local search algorithms operate using a single current state (rather than multiple paths) and generally move only to neighbors of that state.

22. Though local search algorithms are not systematic, key advantages would include __________ a) Less memory b) More time c) Finds a solution in large infinite space d) Less memory & Finds a solution in large infinite space Answer: d Explanation: Two advantages: (1) they use very little memory-usually a constant amount; and (2) they can often find reasonable solutions in large or infinite (continuous) state spaces for which systematic algorithms are unsuitable.

23. A complete, local search algorithm always finds goal if one exists, an optimal algorithm always finds a global minimum/maximum. a) True b) False Answer: a Explanation: An algorithm is complete if it finds a solution if exists and optimal if finds optimal goal (minimum or maximum).

24. _______________ Is an algorithm, a loop that continually moves in the direction of increasing value – that is uphill. a) Up-Hill Search b) Hill-Climbing c) Hill algorithm d) Reverse-Down-Hill search Answer: b Explanation: Refer the definition of Hill-Climbing approach.

25. When will Hill-Climbing algorithm terminate? a) Stopping criterion met b) Global Min/Max is achieved c) No neighbor has higher value d) All of the mentioned Answer: c Explanation: When no neighbor is having higher value, algorithm terminates fetching local min/max.

26. What are the main cons of hill-climbing search? a) Terminates at local optimum & Does not find optimum solution b) Terminates at global optimum & Does not find optimum solution c) Does not find optimum solution & Fail to find a solution d) Fail to find a solution View Answer Answer: a Explanation: Algorithm terminates at local optimum values, hence fails to find optimum solution.

27. Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False Answer: a Explanation: Refer to the definition of variants of hill-climbing search.

28. Hill climbing sometimes called ____________ because it grabs a good neighbor state without thinking ahead about where to go next. a) Needy local search b) Heuristic local search c) Greedy local search d) Optimal local search Answer: c Explanation: None.

29. Hill-Climbing approach stuck for which of the following reasons? a) Local maxima b) Ridges c) Plateaux d) All of the mentioned Answer: d Explanation: Local maxima: a local maximum is a peak that is higher than each of its neighboring states, but lower than the global maximum. Ridges: Ridges result in a sequence of local maxima that is very difficult for greedy algorithms to navigate. Plateaux: a plateau is an area of the state space landscape where the evaluation function is flat.

30. ___________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search Answer: b Explanation: Refer to the definition of Local Beam Search algorithm.

31. A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining two parent states, rather than by modifying a single state. a) True b) False Answer: a Explanation: Stochastic beam search, analogous to stochastic hill climbing, helps to alleviate this problem. Instead of choosing the best k from the pool of candidate successors, stochastic beam search chooses k successors at random, with the probability of choosing a given successor being an increasing function of its value.

32. What are the two main features of Genetic Algorithm? a) Fitness function & Crossover techniques b) Crossover techniques & Random mutation c) Individuals among the population & Random mutation d) Random mutation & Fitness function Answer: a Explanation: Fitness function helps choosing individuals from the population and Crossover techniques defines the offspring generated.

33. Searching using query on Internet is, use of ___________ type of agent. a) Offline agent b) Online agent c) Both Offline & Online agent d) Goal Based & Online agent Answer: d Explanation: Refer to the definitions of both the type of agent.

34. In how many directions do queens attack each other? a) 1 b) 2 c) 3 d) 4

Answer: c Explanation: Queens attack each other in three directions- vertical, horizontal and diagonal.

35. Placing n-queens so that no two queens attack each other is called? a) n-queen’s problem b) 8-queen’s problem c) Hamiltonian circuit problem d) subset sum problem

Answer: a Explanation: Placing n queens so that no two queens attack each other is n-queens problem. If n=8, it is called as 8-queens problem.

36. Where is the n-queens problem implemented? a) carom b) chess c) ludo d) cards

Answer: b Explanation: N-queens problem occurs in chess. It is the problem of placing n- queens in a n*n chess board.

37. Not more than 2 queens can occur in an n-queens problem. a) true b) false

Answer: b Explanation: Unlike a real chess game, n-queens occur in a n-queen problem since it is the problem of dealing with n-queens.

38. In n-queen problem, how many values of n does not provide an optimal solution? a) 1 b) 2 c) 3 d) 4

Answer: b Explanation: N-queen problem does not provide an optimal solution of only three values of n (i.e.) n=2,3.

39. Which of the following methods can be used to solve n-queen’s problem? a) greedy algorithm b) divide and conquer c) iterative improvement d) backtracking

Answer: d Explanation: Of the following given approaches, n-queens problem can be solved using backtracking. It can also be solved using branch and bound.

40. Of the following given options, which one of the following is a correct option that provides an optimal solution for 4-queens problem? a) (3,1,4,2) b) (2,3,1,4) c) (4,3,2,1) d) (4,2,3,1)

Answer: a Explanation: Of the following given options for optimal solutions of 4-queens problem, (3, 1, 4, 2) is the right option.

41. How many possible solutions exist for an 8-queen problem? a) 100 b) 98 c) 92 d) 88

Answer: c Explanation: For an 8-queen problem, there are 92 possible combinations of optimal solutions.

42. How many possible solutions occur for a 10-queen problem? a) 850 b) 742 c) 842 d) 724

Answer: d Explanation: For a 10-queen problem, 724 possible combinations of optimal solutions are available.

43. If n=1, an imaginary solution for the problem exists. a) true b) false

44. What is the domination number for 8-queen’s problem? a) 8 b) 7 c) 6 d) 5

Answer: d Explanation: The minimum number of queens needed to occupy every square in n-queens problem is called domination number. While n=8, the domination number is 5.

45. Of the following given options, which one of the following does not provides an optimal solution for 8-queens problem? a) (5,3,8,4,7,1,6,2) b) (1,6,3,8,3,2,4,7) c) (4,1,5,8,6,3,7,2) d) (6,2,7,1,4,8,5,3)

Answer: b Explanation: The following given options for optimal solutions of 8-queens problem, (1,6,3,8,3,2,4,7) does not provide an optimal solution.

46. General games involves ____________ a) Single-agent b) Multi-agent c) Neither Single-agent nor Multi-agent d) Only Single-agent and M0ulti-agent

Answer: d Explanation: Depending upon games it could be single agent (Sudoku) or multi-agent (Chess).

47. Adversarial search problems uses ____________ a) Competitive Environment b) Cooperative Environment c) Neither Competitive nor Cooperative Environment d) Only Competitive and Cooperative Environment

Answer: a Explanation: Since in cooperative environment agents’ goals are I conflicts. They compete for goal.

48. Mathematical game theory, a branch of economics, views any multi-agent environment as a game provided that the impact of each agent on the others is “significant,” regardless of whether the agents are cooperative or competitive. a) True b) False

Answer: a Explanation: None.

49. Zero sum games are the one in which there are two agents whose actions must alternate and in which the utility values at the end of the game are always the same. a) True b) False

Answer: b Explanation: Utility values are always same and opposite.

50. Zero sum game has to be a ______ game. a) Single player b) Two player c) Multiplayer d) Three player

Answer: c Explanation: Zero sum games could be multiplayer games as long as the condition for zero sum game is satisfied.

51. A game can be formally defined as a kind of search problem with the following components. a) Initial State b) Successor Function c) Terminal Test d) All of the mentioned

Answer: d Explanation: The initial state includes the board position and identifies the player to move. A successor function returns a list of (move, state) pairs, each indicating a legal move and the resulting state. A terminal test determines when the game is over. States where the game has ended are called terminal states. A utility function (also called an objective function or payoff function), which gives a numeric value for the terminal states. In chess, the outcome is a win, lose, or draw, with values +1, -1, or 0.

52. The initial state and the legal moves for each side define the __________ for the game. a) Search Tree b) Game Tree c) State Space Search d) Forest

Answer: b Explanation: An example of game tree for Tic-Tac-Toe game.

53. General algorithm applied on game tree for making decision of win/lose is ____________ a) DFS/BFS Search Algorithms b) Heuristic Search Algorithms c) Greedy Search Algorithms d) MIN/MAX Algorithms

Answer: d Explanation: Given a game tree, the optimal strategy can be determined by examining the min/max value of each node, which we write as MINIMAX- VALUE(n). The min/max value of a node is the utility (for MAX) of being in the corresponding state, assuming that both players play optimally from there to the end of the game. Obviously, the min/max value of a terminal state is just its utility. Furthermore, given a choice, MAX will prefer to move to a state of maximum value, whereas MIN prefers a state of minimum value.

54. The minimax algorithm computes the minimax decision from the current state. It uses a simple recursive computation of the minimax values of each successor state, directly implementing the defining equations. The recursion proceeds all the way down to the leaves of the tree, and then the minimax values are backed up through the tree as the recursion unwinds. a) True b) False

Answer: a Explanation: Refer definition of minimax algorithm.

55. What is the complexity of minimax algorithm? a) Same as of DFS b) Space – bm and time – bm c) Time – bm and space – bm d) Same as BFS

Answer: a Explanation: Same as DFS.

56. Which search is equal to minimax search but eliminates the branches that can’t influence the final decision? a) Depth-first search b) Breadth-first search c) Alpha-beta pruning d) None of the mentioned

Answer: c Explanation: The alpha-beta search computes the same optimal moves as minimax, but eliminates the branches that can’t influence the final decision.

57. Which values are independant in minimax search algorithm? a) Pruned leaves x and y b) Every states are dependant c) Root is independant d) None of the mentioned

Answer: a Explanation: The minimax decision are independant of the values of the pruned values x and y because of the root values.

58. To which depth does the alpha-beta pruning can be applied? a) 10 states b) 8 States c) 6 States d) Any depth

Answer: d Explanation: Alpha–beta pruning can be applied to trees of any depth and it is possible to prune entire subtree rather than leaves.

59. Which search is similar to minimax search? a) Hill-climbing search b) Depth-first search c) Breadth-first search d) All of the mentioned

Answer: b Explanation: The minimax search is depth-first search, So at one time we just have to consider the nodes along a single path in the tree.

60. Which value is assigned to alpha and beta in the alpha-beta pruning? a) Alpha = max b) Beta = min c) Beta = max d) Both Alpha = max & Beta = min

Answer: d Explanation: Alpha and beta are the values of the best choice we have found so far at any choice point along the path for MAX and MIN.

61. Where does the values of alpha-beta search get updated? a) Along the path of search b) Initial state itself c) At the end d) None of the mentioned

Answer: a Explanation: Alpha-beta search updates the value of alpha and beta as it gets along and prunes the remaining branches at node.

62. How the effectiveness of the alpha-beta pruning gets increased? a) Depends on the nodes b) Depends on the order in which they are executed c) All of the mentioned d) None of the mentioned

63. What is called as transposition table? a) Hash table of next seen positions b) Hash table of previously seen positions c) Next value in the search d) None of the mentioned

Answer: b Explanation: Transposition is the occurrence of repeated states frequently in the search.

64. Which is identical to the closed list in Graph search? a) Hill climbing search algorithm b) Depth-first search c) Transposition table d) None of the mentioned

65. Which function is used to calculate the feasibility of whole game tree? a) Evaluation function b) Transposition c) Alpha-beta pruning d) All of the mentioned

Answer: a Explanation: Because we need to cut the search off at some point and apply an evaluation function that gives an estimate of the utility of the state.

1. There exist only two types of quantifiers, Universal Quantification and Existential Quantification. a) True b) False Answer: a Explanation: None.

2. Translate the following statement into FOL. “For every a, if a is a philosopher, then a is a scholar” a) ∀ a philosopher(a) scholar(a) b) ∃ a philosopher(a) scholar(a) c) All of the mentioned d) None of the mentioned Answer: a Explanation: None.

3. A _________ is used to demonstrate, on a purely syntactic basis, that one formula is a logical consequence of another formula. a) Deductive Systems b) Inductive Systems c) Reasoning with Knowledge Based Systems d) Search Based Systems Answer: a Explanation: Refer the definition of Deductive based systems.

4. The statement comprising the limitations of FOL is/are ____________ a) Expressiveness b) Formalizing Natural Languages c) Many-sorted Logic d) All of the mentioned Answer: d Explanation: The Löwenheim–Skolem theorem shows that if a first-order theory has any infinite model, then it has infinite models of every cardinality. In particular, no first-order theory with an infinite model can be categorical. Thus there is no first-order theory whose only model has the set of natural numbers as its domain, or whose only model has the set of real numbers as its domain. Many extensions of first-order logic, including infinitely logics and higher-order logics, are more expressive in the sense that they do permit categorical axiomatizations of the natural numbers or real numbers. This expressiveness comes at a meta-logical cost, however: by Lindström’s theorem, the compactness theorem and the downward Löwenheim–Skolem theorem cannot hold in any logic stronger than first-order. Formalizing Natural Languages : First-order logic is able to formalize many simple quantifier constructions in natural language, such as “every person who lives in Perth lives in Australia”. But there are many more complicated features of natural language that cannot be expressed in (single-sorted) first-order logic. Many-sorted Logic: Ordinary first-order interpretations have a single domain of discourse over which all quantifiers range. Many-sorted first-order logic allows variables to have different sorts, which have different domains.

5. A common convention is: • is evaluated first • and are evaluated next • Quantifiers are evaluated next • is evaluated last. a) True b) False Answer: a Explanation: None.

6. A Term is either an individual constant (a 0-ary function), or a variable, or an n-ary function applied to n terms: F(t1 t2 ..tn). a) True b) False Answer: a Explanation: Definition of term in FOL.

7. First Order Logic is also known as ___________ a) First Order Predicate Calculus b) Quantification Theory c) Lower Order Calculus d) All of the mentioned Answer: d Explanation: None.

8. The adjective “first-order” distinguishes first-order logic from ___________ in which there are predicates having predicates or functions as arguments, or in which one or both of predicate quantifiers or function quantifiers are permitted. a) Representational Verification b) Representational Adequacy c) Higher Order Logic d) Inferential Efficiency Answer: c Explanation: None.

9. Which is created by using single propositional symbol? a) Complex sentences b) Atomic sentences c) Composition sentences d) None of the mentioned Answer: b Explanation: Atomic sentences are indivisible syntactic elements consisting of single propositional symbol.

10. Which is used to construct the complex sentences? a) Symbols b) Connectives c) Logical connectives d) All of the mentioned Answer: c Explanation: None.

11. How many proposition symbols are there in artificial intelligence? a) 1 b) 2 c) 3 d) 4 Answer: b Explanation: The two proposition symbols are true and false.

12. How many logical connectives are there in artificial intelligence? a) 2 b) 3 c) 4 d) 5 Answer: d Explanation: The five logical symbols are negation, conjunction, disjunction, implication and biconditional.

13. Which is used to compute the truth of any sentence? a) Semantics of propositional logic b) Alpha-beta pruning c) First-order logic d) Both Semantics of propositional logic & Alpha-beta pruning Answer: a Explanation: Because the meaning of the sentences is really needed to compute the truth.

14. Which are needed to compute the logical inference algorithm? a) Logical equivalence b) Validity c) Satisfiability d) All of the mentioned Answer: d Explanation: Logical inference algorithm can be solved be using logical equivalence, Validity and satisfiability.

15. From which rule does the modus ponens are derived? a) Inference rule b) Module rule c) Both Inference & Module rule d) None of the mentioned Answer: a Explanation: Inference rule contains the standard pattern that leads to desired goal. The best form of inference rule is modus ponens.

16. Which is also called single inference rule? a) Reference b) Resolution c) Reform d) None of the mentioned Answer: b Explanation: Because resolution yields a complete inference rule when coupled with any search algorithm.

17. Which form is called as a conjunction of disjunction of literals? a) Conjunctive normal form b) Disjunctive normal form c) Normal form d) All of the mentioned Answer: a Explanation: None.

18. What can be viewed as a single lateral of disjunction? a) Multiple clause b) Combine clause c) Unit clause d) None of the mentioned Answer: c Explanation: A single literal can be viewed as a disjunction or one literal also, called a unit clause.

19. Which is a refutation complete inference procedure for propositional logic? a) Clauses b) Variables c) Propositional resolution d) Proposition Answer: c Explanation: Propositional resolution is a refutation complete inference procedure for propositional logic.

20. What kind of clauses are available in Conjunctive Normal Form? a) Disjunction of literals b) Disjunction of variables c) Conjunction of literals d) Conjunction of variables Answer: a Explanation: First-order resolution requires the clause to be in disjunction of literals in Conjunctive Normal Form.

21. What is the condition of literals in variables? a) Existentially quantified b) Universally quantified c) Quantified d) None of the mentioned Answer: b Explanation: Literals that contain variables are assumed to be universally quantified.

22. Which can be converted to inferred equivalent CNF sentence? a) Every sentence of propositional logic b) Every sentence of inference c) Every sentence of first-order logic d) All of the mentioned Answer: c Explanation: Every sentence of first-order logic can be converted to inferred equivalent CNF sentence.

23. Which sentence will be unsatisfiable if the CNF sentence is unsatisfiable? a) Search statement b) Reading statement c) Replaced statement d) Original statement Answer: d Explanation: The CNF statement will be unsatisfiable just when the original sentence is unsatisfiable.

24. Which rule is equal to the resolution rule of first-order clauses? a) Propositional resolution rule b) Inference rule c) Resolution rule d) None of the mentioned Answer: a Explanation: The resolution rule for first-order clauses is simply a lifted version of the propositional resolution rule.

25. At which state does the propositional literals are complementary? a) If one variable is less b) If one is the negation of the other c) All of the mentioned d) None of the mentioned Answer: b Explanation: Propositional literals are complementary if one is the negation of the other.

26. What is meant by factoring? a) Removal of redundant variable b) Removal of redundant literal c) Addition of redundant literal d) Addition of redundant variable Answer: b Explanation: None.

27. What will happen if two literals are identical? a) Remains the same b) Added as three c) Reduced to one d) None of the mentioned Answer: c Explanation: Propositional factoring reduces two literals to one if they are identical.

28. When the resolution is called as refutation-complete? a) Sentence is satisfiable b) Sentence is unsatisfiable c) Sentence remains the same d) None of the mentioned Answer: b Explanation: Resolution is refutation-complete, if a set of sentence is unsatisfiable, then resolution will always be able to derive a contradiction.

29. Which condition is used to cease the growth of forward chaining? a) Atomic sentences b) Complex sentences c) No further inference d) All of the mentioned Answer: c Explanation: Forward chain can grow by adding new atomic sentences until no further inference is made.

30. Which closely resembles propositional definite clause? a) Resolution b) Inference c) Conjunction d) First-order definite clauses Answer: d Explanation: Because they are disjunction of literals of which exactly one is positive.

31. What is the condition of variables in first-order literals? a) Existentially quantified b) Universally quantified c) Both Existentially & Universally quantified d) None of the mentioned Answer: b Explanation: First-order literals will accept variables only if they are universally quantified.

32. Which are more suitable normal form to be used with definite clause? a) Positive literal b) Negative literal c) Generalized modus ponens d) Neutral literal Answer: c Explanation: Definite clauses are a suitable normal form for use with generalized modus ponen.

33. Which will be the instance of the class datalog knowledge bases? a) Variables b) No function symbols c) First-order definite clauses d) None of the mentioned Answer: b Explanation: If the knowledge base contains no function symbols means, it is an instance of the class datalog knowledge base.

34. Which knowledge base is called as fixed point? a) First-order definite clause are similar to propositional forward chaining b) First-order definite clause are mismatch to propositional forward chaining c) All of the mentioned d) None of the mentioned Answer: a Explanation: Fixed point reached by forward chaining with first-order definiteclause are similar to those for propositional forward chaining.

35. How to eliminate the redundant rule matching attempts in the forward chaining? a) Decremental forward chaining b) Incremental forward chaining c) Data complexity d) None of the mentioned Answer: b Explanation: We can eliminate the redundant rule matching attempts in the forward chaining by using incremental forward chaining.

36. From where did the new fact inferred on new iteration is derived? a) Old fact b) Narrow fact c) New fact d) All of the mentioned Answer: c Explanation: None.

37. Which will solve the conjuncts of the rule so that the total cost is minimized? a) Constraint variable b) Conjunct ordering c) Data complexity d) All of the mentioned Answer: b Explanation: Conjunct ordering will find an ordering to solve the conjuncts of the rule premise so that the total cost is minimized.

38. How many possible sources of complexity are there in forward chaining? a) 1 b) 2 c) 3 d) 4 Answer: c Explanation: The three possible sources of complexity are an inner loop, algorithm rechecks every rule on every iteration, algorithm might generate many facts irrelevant to the goal.

39. Which algorithm will work backward from the goal to solve a problem? a) Forward chaining b) Backward chaining c) Hill-climb algorithm d) None of the mentioned Answer: b Explanation: Backward chaining algorithm will work backward from the goal and it will chain the known facts that support the proof.

40. Which is mainly used for automated reasoning? a) Backward chaining b) Forward chaining c) Logic programming d) Parallel programming Answer: c Explanation: Logic programming is mainly used to check the working process of the system.

41. What will backward chaining algorithm will return? a) Additional statements b) Substitutes matching the query c) Logical statement d) All of the mentioned Answer: b Explanation: It will contains the list of goals containing a single element and returns the set of all substitutions satisfying the query.

42. How can be the goal is thought of in backward chaining algorithm? a) Queue b) List c) Vector d) Stack View Answer Answer: d Explanation: The goals can be thought of as stack and if all of them us satisfied means, then current branch of proof succeeds.

43. What is used in backward chaining algorithm? a) Conjuncts b) Substitution c) Composition of substitution d) None of the mentioned Answer: c Explanation: None.

44. Which algorithm are in more similar to backward chaining algorithm? a) Depth-first search algorithm b) Breadth-first search algorithm c) Hill-climbing search algorithm d) All of the mentioned Answer: a Explanation: It is depth-first search algorithm because its space requirements are linear in the size of the proof.

45. Which problem can frequently occur in backward chaining algorithm? a) Repeated states b) Incompleteness c) Complexity d) Both Repeated states & Incompleteness Answer: d Explanation: If there is any loop in the chain means, It will lead to incompleteness and repeated states.

46. How the logic programming can be constructed? a) Variables b) Expressing knowledge in a formal language c) Graph d) All of the mentioned Answer: b Explanation: Logic programming can be constructed by expressing knowledge in a formal expression and the problem can be solved by running inference process.

47. What form of negation does the prolog allows? a) Negation as failure b) Proposition c) Substitution d) Negation as success Answer: a Explanation: None.

48. Which is omitted in prolog unification algorithm? a) Variable check b) Occur check c) Proposition check d) Both Occur & Proposition check Answer: b Explanation: Occur check is omitted in prolog unification algorithm because of unsound inferences.

49. Knowledge and reasoning also play a crucial role in dealing with __________________ environment. a) Completely Observable b) Partially Observable c) Neither Completely nor Partially Observable d) Only Completely and Partially Observable Answer: b Explanation: Knowledge and reasoning could aid to reveal other factors that could complete environment.

50. Treatment chosen by doctor for a patient for a disease is based on _____________ a) Only current symptoms b) Current symptoms plus some knowledge from the textbooks c) Current symptoms plus some knowledge from the textbooks plus experience d) All of the mentioned Answer: c Explanation: None.

51. A knowledge-based agent can combine general knowledge with current percepts to infer hidden aspects of the current state prior to selecting actions. a) True b) False Answer: a Explanation: Refer definition of Knowledge based agents.

52. A) Knowledge base (KB) is consists of set of statements. B) Inference is deriving a new sentence from the KB. Choose the correct option. a) A is true, B is true b) A is false, B is false c) A is true, B is false d) A is false, B is true Answer: a Explanation: None.

53. Wumpus World is a classic problem, best example of _______ a) Single player Game b) Two player Game c) Reasoning with Knowledge d) Knowledge based Game Answer: c Explanation: Refer the definition of Wumpus World Problem.

54. ‘α |= β ‘(to mean that the sentence α entails the sentence β) if and only if, in every model in which α is _____ β is also _____ a) True, true b) True, false c) False, true d) False, false Answer: a Explanation: Refer the definition of law of entailment.

55. Which is not a property of representation of knowledge? a) Representational Verification b) Representational Adequacy c) Inferential Adequacy d) Inferential Efficiency Answer: a Explanation: None.

56. Which is not Familiar Connectives in First Order Logic? a) and b) iff c) or d) not Answer: d Explanation: “not” is coming under propositional logic and is therefore not a connective.

57. Inference algorithm is complete only if _____________ a) It can derive any sentence b) It can derive any sentence that is an entailed version c) It is truth preserving d) It can derive any sentence that is an entailed version & It is truth preserving

58. An inference algorithm that derives only entailed sentences is called sound or truth-preserving. a) True b) False Answer: a Explanation: None.

59. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable. a) True b) False Answer: a Explanation: Rule of universal instantiation.

60. The corresponding Existential Instantiation rule: for the existential quantifier is slightly more complicated. For any sentence a, variable v, and constant symbol k that does not appear elsewhere in the knowledge base. a) True b) False Answer: a Explanation: Rule of existential instantiation.

61. What among the following could the universal instantiation of ___________ For all x King(x) ^ Greedy(x) => Evil(x) a) King(John) ^ Greedy(John) => Evil(John) b) King(y) ^ Greedy(y) => Evil(y) c) King(Richard) ^ Greedy(Richard) => Evil(Richard) d) All of the mentioned Answer: d Explanation: Refer the definition if universal instantiation.

62. Lifted inference rules require finding substitutions that make different logical expressions looks identical. a) Existential Instantiation b) Universal Instantiation c) Unification d) Modus Ponen Answer: c Explanation: None.

63. Which of the following is not the style of inference? a) Forward Chaining b) Backward Chaining c) Resolution Refutation d) Modus Ponen Answer: d Explanation: Modus ponen is a rule for an inference.

64. In order to utilize generalized Modus Ponens, all sentences in the KB must be in the form of Horn sentences. a) True b) False Answer: a Explanation: None.

65. For resolution to apply, all sentences must be in conjunctive normal form, a conjunction of disjunctions of literals. a) True b) False Answer: a Explanation: None.

66. What are the two basic types of inferences? a) Reduction to propositional logic, Manipulate rules directly b) Reduction to propositional logic, Apply modus ponen c) Apply modus ponen, Manipulate rules directly d) Convert every rule to Horn Clause, Reduction to propositional logic Answer: a Explanation: None.

67. Which among the following could the Existential instantiation of ∃x Crown(x) ^ OnHead(x, Johnny)? a) Crown(John) ^ OnHead(John, Jonny) b) Crown(y) ^ OnHead(y, y, x) c) Crown(x) ^ OnHead(x, Jonny) d) None of the mentioned Answer: a Explanation: None.

68. Translate the following statement into FOL. “For every a, if a is a PhD student, then a has a master degree” a) ∀ a PhD(a) -> Master(a) b) ∃ a PhD(a) -> Master(a) c) A is true, B is true d) A is false, B is false Answer: a Explanation: None

69. The rule of Universal Instantiation (UI for short) says that we can infer any sentence obtained by substituting a ground term (a term without variables) for the variable. a) True b) False Answer: a Explanation: Rule of universal instantiation.

70. The corresponding Existential Instantiation rule: for the existential quantifier is slightly more complicated. For any sentence a, variable v, and constant symbol k that does not appear elsewhere in the knowledge base. a) True b) False Answer: a Explanation: Rule of existential instantiation.

1. Which is the most straightforward approach for planning algorithm? a) Best-first search b) State-space search c) Depth-first search d) Hill-climbing search

Answer: b Explanation: The straightforward approach for planning algorithm is state space search because it takes into account of everything for finding a solution.

2. What are taken into account of state-space search? a) Postconditions b) Preconditions c) Effects d) Both Preconditions & Effects

Answer: d Explanation: The state-space search takes both precondition and effects into account for solving a problem.

3. How many ways are available to solve the state-space search? a) 1 b) 2 c) 3 d) 4

Answer: b Explanation: There are two ways available to solve the state-space search. They are forward from the initial state and backward from the goal.

4. What is the other name for forward state-space search? a) Progression planning b) Regression planning c) Test planning d) None of the mentioned

Answer: a Explanation: It is sometimes called as progression planning, because it moves in the forward direction.

5. How many states are available in state-space search? a) 1 b) 2 c) 3 d) 4

Answer: d Explanation: There are four states available in state-space search. They are initial state, actions, goal test and step cost.

6. What is the main advantage of backward state-space search? a) Cost b) Actions c) Relevant actions d) All of the mentioned

Answer: c Explanation: The main advantage of backward search will allow us to consider only relevant actions.

7. What is the other name of the backward state-space search? a) Regression planning b) Progression planning c) State planning d) Test planning

Answer: a Explanation: Backward state-space search will find the solution from goal to the action, So it is called as Regression planning.

8. What is meant by consistent in state-space search? a) Change in the desired literals b) Not any change in the literals c) No change in goal state d) None of the mentioned

Answer: b Explanation: Consistent means that the completed actions will not undo any desired literals.

9. What will happen if a predecessor description is generated that is satisfied by the initial state of the planning problem? a) Success b) Error c) Compilation d) Termination

Answer: d Explanation: None.

10. Which approach is to pretend that a pure divide and conquer algorithm will work? a) Goal independence b) Subgoal independence c) Both Goal & Subgoal independence d) None of the mentioned

Answer: b Explanation: Subgoal independence approach is to pretend that a pure divide and conquer algorithm will work for admissible heuristics.

11. The process by which the brain incrementally orders actions needed to complete a specific task is referred as ______________ a) Planning problem b) Partial order planning c) Total order planning d) Both Planning problem & Partial order planning

Answer: b Explanation: Definition of partial order planning.

12. To complete any task, the brain needs to plan out the sequence by which to execute the behavior. One way the brain does this is with a partial-order plan. a) True b) False

13. In partial order plan. A. Relationships between the actions of the behavior are set prior to the actions B. Relationships between the actions of the behavior are not set until absolutely necessary Choose the correct option. a) A is true b) B is true c) Either A or B can be true depending upon situation d) Neither A nor B is true

Answer: a Explanation: Relationship between behavior and actions is established dynamically.

14. Partial-order planning exhibits the Principle of Least Commitment, which contributes to the efficiency of this planning system as a whole. a) True b) False

15. Following is/are the components of the partial order planning. a) Bindings b) Goal c) Causal Links d) All of the mentioned

Answer: d Explanation: Bindings: The bindings of the algorithm are the connections between specific variables in the action. Bindings, as ordering, only occur when it is absolutely necessary. Causal Links: Causal links in the algorithm are those that categorically order actions. They are not the specific order (1,2,3) of the actions, rather the general order as in Action 2 must come somewhere after Action 1, but before Action 2. Plan Space: The plan space of the algorithm is constrained between its start and finish. The algorithm starts, producing the initial state and finishes when all parts of the goal is been achieved.

16. Partial-order planning is the opposite of total-order planning. a) True b) False

Answer: a Explanation: Partial-order planning is the opposite of total-order planning, in which actions are sequenced all at once and for the entirety of the task at hand.

17. Sussman Anomaly can be easily and efficiently solved by partial order planning. a) True b) False

Answer: a Explanation: http://en.wikipedia.org/wiki/Sussman_Anomaly.

18. Sussman Anomaly illustrates a weakness of interleaved planning algorithm. a) True b) False

Answer: b Explanation: Sussman Anomaly illustrates a weakness of non interleaved planning algorithm.

19. One the main drawback of this type of planning system is that it requires a lot of computational powers at each node. a) True b) False

20. What are you predicating by the logic: ۷x: €y: loyalto(x, y). a) Everyone is loyal to someone b) Everyone is loyal to all c) Everyone is not loyal to someone d) Everyone is loyal

Answer: a Explanation: ۷x denotes Everyone or all, and €y someone and loyal to is the proposition logic making map x to y.

21. A plan that describe how to take actions in levels of increasing refinement and specificity is ____________ a) Problem solving b) Planning c) Non-hierarchical plan d) Hierarchical plan

Answer: d Explanation: A plan that describes how to take actions in levels of increasing refinement and specificity is Hierarchical (e.g., “Do something” becomes the more specific “Go to work,” “Do work,” “Go home.”) Most plans are hierarchical in nature.

22. A constructive approach in which no commitment is made unless it is necessary to do so, is ____________ a) Least commitment approach b) Most commitment approach c) Nonlinear planning d) Opportunistic planning

Answer: a Explanation: Because we are not sure about the outcome.

23. Uncertainty arises in the Wumpus world because the agent’s sensors give only ____________ a) Full & Global information b) Partial & Global Information c) Partial & local Information d) Full & local information

Answer: c Explanation: The Wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent (you) always starts in the lower left corner, a square that will be labeled [1, 1]. The agent’s task is to find the gold, return to [1, 1] and climb out of the cave. Therefore, uncertainty is there as the agent gives partial and local information only. Global variable are not goal specific problem solving.

24. Which of the following search belongs to totally ordered plan search? a) Forward state-space search b) Hill-climbing search c) Depth-first search d) Breadth-first search

Answer: a Explanation: Forward and backward state-space search are particular forms of totally ordered plan search.

25. Which cannot be taken as advantage for totally ordered plan search? a) Composition b) State search c) Problem decomposition d) None of the mentioned

Answer: c Explanation: As the search explore only linear sequences of actions, So they cannot take advantage of problem decomposition.

26. What is the advantage of totally ordered plan in constructing the plan? a) Reliability b) Flexibility c) Easy to use d) All of the mentioned

Answer: b Explanation: Totally ordered plan has the advantage of flexibility in the order in which it constructs the plan.

27. Which strategy is used for delaying a choice during search? a) First commitment b) Least commitment c) Both First & Least commitment d) None of the mentioned

Answer: b Explanation: The general strategy of delaying a choice during search is called the least commitment strategy.

28. Which algorithm places two actions into a plan without specifying which should come first? a) Full-order planner b) Total-order planner c) Semi-order planner d) Partial-order planner

Answer: d Explanation: Any planning algorithm that can place two actions into a plan without specifying which should come first is called partial-order planner.

29. How many possible plans are available in partial-order solution? a) 3 b) 4 c) 5 d) 6

Answer: d Explanation: The partial-order solution corresponds to six possible total-order plans.

30. What is the other name of each and every total-order plans? a) Polarization b) Linearization c) Solarization d) None of the mentioned Answer: b Explanation: Each and every total order plan is also called as linearization of the partial-order plan.

31. What are present in the empty plan? a) Start b) Finish c) Modest d) Both Start & Finish View Answer

32. What are not present in start actions? a) Preconditions b) Effect c) Finish d) None of the mentioned Answer: a Explanation: Start has no precondition and has as its effects all the literals in the initial state of the planning problem.

33. What are not present in finish actions? a) Preconditions b) Effect c) Finish d) None of the mentioned Answer: b Explanation: Finish has no effects and has as its preconditions the goal literals of the planning algorithm.

34. Which can be adapted for planning algorithms? a) Most-constrained variable b) Most-constrained literal c) Constrained d) None of the mentioned Answer: a Explanation: The most-constrained variable heuristic from CSPs can be adapted for planning algorithm and seems to work well.

1. Using logic to represent and reason we can represent knowledge about the world with facts and rules. a) True b) False

2. Uncertainty arises in the wumpus world because the agent’s sensors give only ___________ a) Full & Global information b) Partial & Global Information c) Partial & local Information d) Full & local information

Answer: c Explanation: The Wumpus world is a grid of squares surrounded by walls, where each square can contain agents and objects. The agent (you) always starts in the lower left corner, a square that will be labeled [1, 1]. The agent’s task is to find the gold, return to [1, 1] and climb out of the cave. So uncertainty is there as the agent gives partial and local information only. Global variable are not goal specific problem solving.

3. A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable d) Both Discrete and Discontinuous variable

Answer: a Explanation: To specify a Hybrid network, we have to specify two new kinds of distributions: the conditional distribution for continuous variables given discrete or continuous parents, and the conditional distribution for a discrete variable given continuous parents.

4. How is Fuzzy Logic different from conventional control methods? a) IF and THEN Approach b) FOR Approach c) WHILE Approach d) DO Approach

Answer: a Explanation: FL incorporates a simple, rule-based IF X AND Y THEN Z approach to a solving control problem rather than attempting to model a system mathematically.

5. If a hypothesis says it should be positive, but in fact it is negative, we call it ___________ a) A consistent hypothesis b) A false negative hypothesis c) A false positive hypothesis d) A specialized hypothesis

Answer: c Explanation: Consistent hypothesis go with examples, If the hypothesis says it should be negative but in fact it is positive, it is false negative. If a hypothesis says it should be positive, but in fact it is negative, it is false positive. In a specialized hypothesis we need to have certain restrict or special conditions.

6. The primitives in probabilistic reasoning are random variables. a) True b) False

Answer: a Explanation: The primitives in probabilistic reasoning are random variables. Just like primitives in Propositional Logic are propositions. A random variable is not in fact a variable, but a function from a sample space S to another space, often the real numbers.

7. Which is true for Decision theory? a) Decision Theory = Probability theory + utility theory b) Decision Theory = Inference theory + utility theory c) Decision Theory = Uncertainty + utility theory d) Decision Theory = Probability theory + preference

8. A constructive approach in which no commitment is made unless it is necessary to do so is ___________ a) Least commitment approach b) Most commitment approach c) Nonlinear planning d) Opportunistic planning

9. What is the extraction of the meaning of utterance? a) Syntactic b) Semantic c) Pragmatic d) None of the mentioned

Answer: b Explanation: Semantic analysis is used to extract the meaning from the group of sentences.

10. What is the process of associating a FOL expression with a phrase? a) Interpretation b) Augmented reality c) Semantic interpretation d) Augmented interpretation

Answer: c Explanation: Semantic interpretation is the process of associating a FOL expression with a phrase.

11. What is meant by compositional semantics? a) Determining the meaning b) Logical connectives c) Semantics d) None of the mentioned

Answer: a Explanation: Compositional semantics is the process of determining the meaning of P*Q from P, Q and *.

12. What is used to augment a grammar for arithmetic expression with semantics? a) Notation b) DCG notation c) Constituent d) All of the mentioned

Answer: b Explanation: DCG notation is used to augment a grammar for arithmetic expression with semantics and it is used to build a parse tree.

13. What can’t be done in the semantic interpretation? a) Logical term b) Complete logical sentence c) Both Logical term & Complete logical sentence d) None of the mentioned

Answer: c Explanation: Some kind of sentence in the semantic interpretation can’t be logical term nor a complete logical sentence.

14. How many verb tenses are there in the English language? a) 1 b) 2 c) 3 d) 4

Answer: c Explanation: There are three types of tenses available in english language are past, present and future.

15. Which is used to mediate between syntax and semantics? a) Form b) Intermediate form c) Grammer d) All of the mentioned

16. What is meant by quasi-logical form? a) Sits between syntactic and logical form b) Logical connectives c) All of the mentioned d) None of the mentioned

Answer: a Explanation: It can be translated into a regular first-order logical sentence, So that it Sits between syntactic and logical form.

17. How many types of quantification are available in artificial intelligence? a) 1 b) 2 c) 3 d) 4

Answer: b Explanation: There are two types of quantification available. They are universal and existential.

18. What kind of interpretation is done by adding context-dependant information? a) Semantic b) Syntactic c) Pragmatic d) None of the mentioned

19. How many issues are available in describing degree of belief? a) 1 b) 2 c) 3 d) 4

Answer: b Explanation: The main issues for degree of belief are nature of the sentences and the dependance of degree of the belief.

20. What is used for probability theory sentences? a) Conditional logic b) Logic c) Extension of propositional logic d) None of the mentioned

Answer: c Explanation: The version of probability theory we present uses an extension of propositional logic for its sentences.

21. Where does the dependance of experience is reflected in prior probability sentences? a) Syntactic distinction b) Semantic distinction c) Both Syntactic & Semantic distinction d) None of the mentioned

Answer: a Explanation: The dependance on experience is reflected in the syntactic distinction between prior probability statements.

22. Where does the degree of belief is applied? a) Propositions b) Literals c) Variables d) Statements

23. How many formal languages are used for stating propositions? a) 1 b) 2 c) 3 d) 4

Answer: b Explanation: The two formal languages used for stating propositions are propositional logic and first-order logic.

24. What is the basic element of a language? a) Literal b) Variable c) Random variable d) All of the mentioned

Answer: c Explanation: The basic element for a language is the random variable, which can be thought as a part of world and its status is initially unknown.

25. How many types of random variables are available? a) 1 b) 2 c) 3 d) 4

Answer: c Explanation: The three types of random variables are boolean, discrete and continuous.

26. Which is the complete specification of the state of the world? a) Atomic event b) Complex event c) Simple event d) None of the mentioned

Answer: a Explanation: An atomic event is the complete specification of the state of the world about which the event is uncertain.

27. Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Answer: b Explanation: For continuous variables, it is not possible to write out the entire distribution as a table.

28. What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

29. How many terms are required for building a bayes model? a) 1 b) 2 c) 3 d) 4

Answer: c Explanation: The three required terms are a conditional probability and two unconditional probability.

30. What is needed to make probabilistic systems feasible in the world? a) Reliability b) Crucial robustness c) Feasibility d) None of the mentioned

Answer: b Explanation: On a model-based knowledge provides the crucial robustness needed to make probabilistic system feasible in the real world.

31. Where does the bayes rule can be used? a) Solving queries b) Increasing complexity c) Decreasing complexity d) Answering probabilistic query

Answer: d Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.

32. What does the bayesian network provides? a) Complete description of the domain b) Partial description of the domain c) Complete description of the problem d) None of the mentioned

Answer: a Explanation: A Bayesian network provides a complete description of the domain.

33. How the entries in the full joint probability distribution can be calculated? a) Using variables b) Using information c) Both Using variables & information d) None of the mentioned

Answer: b Explanation: Every entry in the full joint probability distribution can be calculated from the information in the network.

34. How the bayesian network can be used to answer any query? a) Full distribution b) Joint distribution c) Partial distribution d) All of the mentioned

Answer: b Explanation: If a bayesian network is a representation of the joint distribution, then it can solve any query, by summing all the relevant joint entries.

35. How the compactness of the bayesian network can be described? a) Locally structured b) Fully structured c) Partial structure d) All of the mentioned

Answer: a Explanation: The compactness of the bayesian network is an example of a very general property of a locally structured system.

36. To which does the local structure is associated? a) Hybrid b) Dependant c) Linear d) None of the mentioned

Answer: c Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

37. Which condition is used to influence a variable directly by all the others? a) Partially connected b) Fully connected c) Local connected d) None of the mentioned

38. What is the consequence between a node and its predecessors while creating bayesian network? a) Functionally dependent b) Dependant c) Conditionally independent d) Both Conditionally dependant & Dependant

1. What is the field of Natural Language Processing (NLP)? a) Computer Science b) Artificial Intelligence c) Linguistics d) All of the mentioned

2. NLP is concerned with the interactions between computers and human (natural) languages. a) True b) False

Answer: a Explanation: NLP has its focus on understanding the human spoken/written language and converts that interpretation into machine understandable language.

3. What is the main challenge/s of NLP? a) Handling Ambiguity of Sentences b) Handling Tokenization c) Handling POS-Tagging d) All of the mentioned

Answer: a Explanation: There are enormous ambiguity exists when processing natural language.

4. Modern NLP algorithms are based on machine learning, especially statistical machine learning. a) True b) False

5. Choose form the following areas where NLP can be useful. a) Automatic Text Summarization b) Automatic Question-Answering Systems c) Information Retrieval d) All of the mentioned

6. Which of the following includes major tasks of NLP? a) Automatic Summarization b) Discourse Analysis c) Machine Translation d) All of the mentioned

Answer: d Explanation: There is even bigger list of tasks of NLP. http://en.wikipedia.org/wiki/Natural_language_processing#Major_tasks_in_NLP.

7. What is Coreference Resolution? a) Anaphora Resolution b) Given a sentence or larger chunk of text, determine which words (“mentions”) refer to the same objects (“entities”) c) All of the mentioned d) None of the mentioned

Answer: b Explanation: Anaphora resolution is a specific type of coreference resolution.

8. What is Machine Translation? a) Converts one human language to another b) Converts human language to machine language c) Converts any human language to English d) Converts Machine language to human language

Answer: a Explanation: The best known example of machine translation is google translator.

9. The more general task of coreference resolution also includes identifying so-called “bridging relationships” involving referring expressions. a) True b) False

Answer: a Explanation: Refer the definition of Coreference Resolution.

10. What is Morphological Segmentation? a) Does Discourse Analysis b) Separate words into individual morphemes and identify the class of the morphemes c) Is an extension of propositional logic d) None of the mentioned

11. Given a stream of text, Named Entity Recognition determines which pronoun maps to which noun. a) False b) True

Answer: a Explanation: Given a stream of text, Named Entity Recognition determines which items in the text maps to proper names.

12. Natural Language generation is the main task of Natural language processing. a) True b) False

Answer: a Explanation: Natural Language Generation is to Convert information from computer databases into readable human language.

13. OCR (Optical Character Recognition) uses NLP. a) True b) False

Answer: a Explanation: Given an image representing printed text, determines the corresponding text.

14. Parts-of-Speech tagging determines ___________ a) part-of-speech for each word dynamically as per meaning of the sentence b) part-of-speech for each word dynamically as per sentence structure c) all part-of-speech for a specific word given as input d) all of the mentioned

Answer: d Explanation: A Bayesian network provides a complete description of the domain.

15. Parsing determines Parse Trees (Grammatical Analysis) for a given sentence. a) True b) False

Answer: a Explanation: Determine the parse tree (grammatical analysis) of a given sentence. The grammar for natural languages is ambiguous and typical sentences have multiple possible analyses. In fact, perhaps surprisingly, for a typical sentence there may be thousands of potential parses (most of which will seem completely nonsensical to a human).

16. IR (information Retrieval) and IE (Information Extraction) are the two same thing. a) True b) False

Answer: b Explanation: Information retrieval (IR) – This is concerned with storing, searching and retrieving information. It is a separate field within computer science (closer to databases), but IR relies on some NLP methods (for example, stemming). Some current research and applications seek to bridge the gap between IR and NLP. Information extraction (IE) – This is concerned in general with the extraction of semantic information from text. This covers tasks such as named entity recognition, Coreference resolution, relationship extraction, etc.

17. Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by ____________ a) Fuzzy Logic b) Word Sense Disambiguation c) Shallow Semantic Analysis d) All of the mentioned

Answer: b Explanation: Shallow Semantic Analysis doesn’t cover word sense disambiguation.

18. Given a sound clip of a person or people speaking, determine the textual representation of the speech. a) Text-to-speech b) Speech-to-text c) All of the mentioned d) None of the mentioned

Answer: b Explanation: NLP is required to linguistic analysis.

19. Speech Segmentation is a subtask of Speech Recognition. a) True b) False

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