set-2

Answer: 1. Deterministic

Explanation:

• In a deterministic environment, the next state is entirely determined by the current state and the agent’s actions.
• There is no randomness or uncertainty in the outcome.

Answer: 3. Deterministic

Explanation:

• In Tic-Tac-Toe, the next state of the game is entirely determined by the current state and the player’s move.
• This makes it a deterministic environment.

Answer: 1. Non-deterministic/Stochastic

Explanation:

• Self-driving vehicles operate in non-deterministic or stochastic environments because they must deal with unpredictable factors like other drivers, pedestrians, and weather conditions.
• The outcomes of their actions are not always certain.

Answer: 2. Episodes

Explanation:

• In an episodic environment, the agent’s experience is divided into episodes, where each episode is independent of the others.
• Actions taken in one episode do not affect the next.

Answer: 1. Episodic Environment

Explanation:

• An email sorting system operates in an episodic environment because each email is processed independently.
• The sorting of one email does not affect the sorting of the next.

Answer: 3. Non-Episodic Environment

Explanation:

• A chess game is a non-episodic environment because each move affects the state of the game and influences future moves.
• The game is continuous and interconnected.

Answer: 1. Sequential/Non-Episodic

Explanation:

• In a sequential or non-episodic environment, current decisions affect future decisions, and the agent must consider the history of its actions.
• This is common in games like chess or real-world planning tasks.

Answer: 1. Static, Dynamic

Explanation:

• A static environment does not change while the agent is acting, whereas a dynamic environment changes over time.
• This distinction is important for designing agents that can adapt to their surroundings.

Answer: 1. Static

Explanation:

• The equation $2 + 2 = 4$ is a static environment because it does not change over time.
• The result is always the same, regardless of when or how it is calculated.

Answer: 2. Dynamic

Explanation:

• A football game is a dynamic environment because the state of the game changes continuously with each action.
• Players must constantly adapt to new situations.

Answer: 1. Discrete

Explanation:

• A discrete environment has a finite number of distinct states, each clearly defined.
• This is in contrast to a continuous environment, where states can vary infinitely.

Answer: 1. Discrete

Explanation:

• Games like chess and checkers have a finite number of possible moves and states, making them discrete environments.
• The game progresses through a series of distinct states.

Answer: 2. Continuous

Explanation:

• A continuous environment involves signals and actions that change continuously over time.
• This is common in real-world systems like self-driving cars or robotics.

Answer: 2. Continuous

Explanation:

• Taxi driving involves navigating through a continuous environment, where routes and positions can vary infinitely.
• The environment is not limited to a finite set of states.

Answer: 4. No agent / Multiple agents

Explanation:

• The properties of an environment include whether it is discrete/continuous, static/dynamic, and deterministic/non-deterministic.
• The presence or absence of agents is not a property of the environment itself.

Answer: 2. Static

Explanation:

• A crossword puzzle is a static environment because the puzzle does not change while the solver is working on it.
• The solver’s actions do not alter the puzzle itself.

Answer: 3. Environment does not change with the passage of time, but Agent performance changes

Explanation:

• A semi-dynamic environment is one where the environment itself does not change over time, but the agent’s performance or actions may change.
• This is a hybrid between static and dynamic environments.

Answer: 1. Fully observable environment

Explanation:

• If an agent’s sensors provide complete access to the environment’s state at all times, the environment is fully observable.
• The agent has all the information it needs to make decisions.

Answer: 2. Dynamic Environment

Explanation:

• In a dynamic environment, the environment can change while the agent is thinking or acting.
• This requires the agent to adapt to new situations in real-time.

Answer: 2. Semi-Dynamic Environment

Explanation:

• A semi-dynamic environment is one where the environment itself does not change over time, but the agent’s performance or score may change.
• This is a hybrid between static and dynamic environments.

Answer: 1. Single Agent

Explanation:

• A single agent operates independently in an environment without interacting with other agents.
• This is in contrast to a multi-agent system, where multiple agents interact.

Answer: 4. Intelligent goal-based agent

Explanation:

• A web crawler is an intelligent goal-based agent because it is designed to achieve specific goals, such as indexing web pages.
• It uses intelligent algorithms to navigate and collect data from the web.

Answer: 3. All of the mentioned

Explanation:

• The main task of a problem-solving agent is to solve the given problem and reach the goal.
• This involves finding the sequence of actions that will lead to the goal state.

Answer: 2. Problem Solving

Explanation:

• Problem solving involves generating solutions based on observed data or information.
• It is a key task for intelligent agents in AI.

Answer: 4. All of the mentioned above

Explanation:

• Problem solving is characterized by a set of goals, a set of objects, and a set of operations that can be performed to achieve the goals.
• These elements define the problem space.

Answer: 2. Abstract

Explanation:

• The problem space is an abstract representation of the problem, including all possible states and actions.
• It is used by the agent to explore and find solutions.

Answer: 1. Combination of operations and objects that achieve the goals.

Explanation:

• The solution to a problem space involves finding the combination of operations and objects that achieve the desired goals.
• This is the essence of problem-solving in AI.

Answer: 2. Solution

Explanation:

• Search in AI refers to the process of finding a solution within the problem space.
• The agent explores possible states and actions to reach the goal.

Answer: 5. All of above mentioned

Explanation:

• To build a problem-solving system, we need to define the problem, analyze it, represent the necessary knowledge, and choose the best technique to solve it.
• These steps are essential for effective problem-solving in AI.

Answer: 2. Problem

Explanation:

• A problem is defined by its elements (e.g., objects, goals) and the relations between them.
• This definition helps the agent understand and solve the problem.

Answer: 1. State

Explanation:

• A state represents the configuration of elements in the problem space at a given moment.
• It is a snapshot of the problem at a specific point in time.

Answer: 1. Successor function

Explanation:

• The successor function is used to generate new states from the current state by applying actions.
• It is essential for exploring the problem space and finding solutions.

Answer: 2. State space

Explanation:

• The state space is the set of all possible states that can be reached from the initial state by applying actions.
• It represents the entire problem space that the agent can explore.

Answer: 2. Tree and Graph

Explanation:

• The state space can be represented as a tree or a graph, depending on the problem.
• Trees are used for problems with a hierarchical structure, while graphs are used for more complex relationships.

Answer: 2. Search process

Explanation:

• The search process explores the state space to find a path from the initial state to the goal state.
• It involves evaluating and selecting actions to reach the solution.

Answer: 2. Worst case

Explanation:

• In the worst case, the search process explores all possible paths between the initial state and the goal state.
• This occurs when the search algorithm does not use heuristics or other optimizations.

Answer: 2. Solution

Explanation:

• A solution in the state space is a path from the initial state to the goal state.
• It represents the sequence of actions that solve the problem.

Answer: 4. All of them

Explanation:

• A problem consists of the current state, the actions that can be taken, and the desired state (goal).
• These elements define the problem and guide the search for a solution.

Answer: 2. Problem Instance

Explanation:

• In search terminology, the combination of the initial state and the goal state is called a problem instance.
• It defines the specific problem to be solved.

Answer: 4. Abstraction

Explanation:

• Abstraction is the process of removing unnecessary details from a state representation to simplify the problem.
• It helps focus on the essential aspects of the problem.

Answer: 2. State space problem

Explanation:

• A state space problem is deterministic, fully observable, known, and discrete.
• These properties make it easier to model and solve using search algorithms.

Answer: 3. Conformant Problem

Explanation:

• A conformant problem is one where the agent cannot observe the state of the environment directly.
• The agent must act based on incomplete or uncertain information.

Answer: 4. Contingency Problem

Explanation:

• A contingency problem involves non-deterministic and/or partially observable environments.
• The agent must handle uncertainty and adapt to changing conditions.

Answer: 1. Exploration problem

Explanation:

• An exploration problem involves an unknown state space, where the agent must explore and learn about the environment as it acts.
• This is common in reinforcement learning and robotics.

Answer: 4. All of above

Explanation:

• A well-defined problem includes the initial state, available actions, goal test, and path cost.
• These components are necessary for defining and solving the problem.

Answer: 1. Completeness, Time Complexity, Space Complexity, Optimality

Explanation:

• Search algorithms are evaluated based on completeness (whether they find a solution if one exists), time complexity (how long it takes to find a solution), space complexity (how much memory is required), and optimality (whether the solution is the best possible).
• These criteria help determine the efficiency and effectiveness of the algorithm.

Answer: 1. Time, Space Complexity

Explanation:

• Time complexity and space complexity are measured in terms of the branching factor (b), depth of the least-cost solution (d), and maximum depth of the search tree (m).
• These parameters help analyze the efficiency of search algorithms.

Answer: 2. Uninformed Search

Explanation:

• Uninformed search techniques, such as breadth-first search and depth-first search, do not use additional information about the distance to the goal.
• They explore the state space blindly.

Answer: 1. Informed Search

Explanation:

• Informed search techniques, such as A* search, use additional information (heuristics) to estimate the distance to the goal.
• This helps guide the search more efficiently.

Answer: 1. Informed Search

Explanation:

• Informed search uses knowledge, such as heuristics, to find the steps to the solution.
• This makes the search process more efficient compared to uninformed search.

Answer: 4. 10 expanded nodes are: S, S, A, B, C, S, A, D, E, G

Explanation:

• Iterative Deepening Search combines DFS and BFS by iteratively increasing the depth limit.
• The order of expansion would be: S → S → A → B → C → S → A → D → E → G.