set-4

Answer: 4. All of the above

Explanation:

• Hill Climbing is a generate-and-test variant that does not backtrack.
• It uses a greedy approach to move towards the best neighbor state.

Answer: 4. All of mentioned above

Explanation:

• Hill Climbing can get stuck in local maxima, plateaus, and ridges.
• These are common problems that prevent the algorithm from finding the global optimum.

Answer: 2. Simulated Annealing

Explanation:

• Simulated Annealing allows some “bad” moves to escape local maxima.
• The frequency of these moves decreases over time, helping the algorithm converge to the global optimum.

Answer: 2. Global

Explanation:

• Simulated Annealing is a global optimization technique.
• It is designed to find the global optimum by exploring the search space more thoroughly.

Answer: 2. Memory less

Explanation:

• Simulated Annealing is a memoryless algorithm.
• It does not use any information gathered during the search to guide future moves.

Answer: 2. Monte-Carlo simulation

Explanation:

• The Metropolis Algorithm used in Simulated Annealing is based on Monte-Carlo simulation techniques.
• It uses random sampling to explore the search space.

Answer: 2. Competitive

Explanation:

• Adversarial Search is used in competitive environments where multiple agents compete against each other.
• Examples include games like chess and tic-tac-toe.

Answer: 3. Only single and multi

Explanation:

• General games can involve either single-agent or multi-agent scenarios.
• The nature of the game determines the number of agents involved.

Answer: 2. Single-agent

Explanation:

• Single-agent search methods focus on how close the agent is to the goal state.
• They do not consider the actions of other agents.

Answer: 2. Two-player

Explanation:

• In two-player games, the decisions of both agents affect the search space.
• Each agent’s move influences the possible moves of the other agent.

Answer: 2. MAX, MIN

Explanation:

• In two-player games, the agent is often called MAX (aiming to maximize its score), and the opponent is called MIN (aiming to minimize the agent’s score).
• This is the basis of the Minimax Algorithm.

Answer: 2. Two-player (Computer and User)

Explanation:

• The Minimax Algorithm is perfect for deterministic two-player games.
• It is commonly used in games like chess and tic-tac-toe.

Answer: 2. Deterministic Single player

Explanation:

• Free cell, 8-puzzle, and Rubik’s cube are examples of deterministic single-player games.
• The outcome is determined solely by the player’s actions.

Answer: 1. Deterministic Multi player

Explanation:

• Tic-tac-toe, chess, and checkers are examples of deterministic multi-player games.
• The outcome depends on the actions of both players.

Answer: 3. Two-player

Explanation:

• In two-player games, one player aims to maximize their result, while the other aims to minimize it.
• This is the basis of the Minimax Algorithm.

Answer: 1. $O(b^m)$, $O(bm)$

Explanation:

• The time complexity of the Minimax Algorithm is $O(b^m)$, where $b$ is the branching factor and $m$ is the maximum depth.
• The space complexity is $O(bm)$ because it stores the current path in the stack.

Answer: 2. Depth-first

Explanation:

• The Minimax Algorithm performs a depth-first exploration of the game tree.
• It explores each branch to the maximum depth before backtracking.

Answer: 3. Yes

Explanation:

• Minimax Search is complete if the game tree is finite.
• It will eventually find the optimal solution if one exists.

Answer: 3. Mini Max Algorithm

Explanation:

• The Minimax Algorithm is used to make decisions about winning or losing in game trees.
• It evaluates all possible moves to determine the best strategy.

Answer: 2. Does not affect

Explanation:

• Alpha-Beta Pruning does not affect the final result.
• It only reduces the number of nodes evaluated, improving efficiency without changing the outcome.

Answer: 3. Highest value

Explanation:

• In Alpha-Beta Pruning, $\alpha$ represents the highest value choice found so far for the MAX player.
• $\beta$ represents the lowest value choice found so far for the MIN player.

Answer: 2. Alpha beta pruning

Explanation:

• Alpha-Beta Pruning is a modified version of the Minimax Algorithm.
• It reduces the number of nodes evaluated by pruning branches that cannot influence the final decision.

Answer: 4. Any depth

Explanation:

• Alpha-Beta Pruning can be applied to any depth in the game tree.
• It is not limited to a specific number of states or levels.

Answer: 1. Negative Infinity, Positive Infinity

Explanation:

• In Alpha-Beta Pruning, the initial value of $\alpha$ is negative infinity, and $\beta$ is positive infinity.
• These values are updated as the algorithm explores the game tree.

Answer: 1. alpha>=beta

Explanation:

• The main condition for Alpha-Beta Pruning is $\alpha \geq \beta$.
• When this condition is met, the algorithm prunes the remaining branches.

Answer: 2. Worst ordering, Ideal Ordering

Explanation:

• The two types of move ordering in Alpha-Beta Pruning are worst ordering and ideal ordering.
• Ideal ordering maximizes pruning efficiency, while worst ordering minimizes it.

Answer: 1. $O(b^{m/2})$

Explanation:

• With perfect ordering, the time complexity of Alpha-Beta Pruning is $O(b^{m/2})$.
• This significantly reduces the number of nodes evaluated compared to Minimax.

Answer: 4. All of above

Explanation:

• In AI, knowledge can be procedural, declarative, meta, structural, or heuristic.
• These types of knowledge are used to represent and reason about information in AI systems.

Answer: 1. Procedural

Explanation:

• Procedural Knowledge is also known as Imperative Knowledge.
• It involves knowing how to perform tasks or procedures.

Answer: 2. Declarative Knowledge

Explanation:

• Declarative Knowledge is non-procedural and independent of specific targets or problem-solving methods.
• It involves knowing facts and information.

Answer: 3. Meta Knowledge

Explanation:

• Meta Knowledge is knowledge about knowledge and how to acquire it.
• It involves understanding the nature and structure of knowledge itself.

Answer: 3. Heuristic Knowledge

Explanation:

• Heuristic Knowledge represents the knowledge of experts in a field or subject.
• It involves rules of thumb and practical strategies for problem-solving.

Answer: 4. Structural Knowledge

Explanation:

• Structural Knowledge describes the relationships between concepts or objects.
• It involves understanding how different elements are connected.

Answer: 2. Complex real-life problems

Explanation:

• Knowledge Representation and Reasoning is used to represent real-world information in a way that computers can understand.
• This knowledge is then utilized to solve complex real-life problems.

Answer: 4. All of above

Explanation:

• In AI, knowledge about objects, events, performance, facts, knowledge bases, and meta knowledge needs to be represented.
• These types of knowledge are essential for reasoning and problem-solving.

Answer: 4. All of above

Explanation:

• Logical Representation, Semantic Networks, Frame Representation, and Production Rules are all techniques used for knowledge representation in AI.
• These techniques help in organizing and reasoning about knowledge.

Answer: 2. Logical Representation

Explanation:

• Logical Representation is a language with definite rules that deal with propositions.
• It ensures there is no ambiguity in the representation of knowledge.

Answer: 2. Logical Representation

Explanation:

• Logical Representation is used to give information to agents and retrieve it without errors.
• It provides a clear and unambiguous way to represent knowledge.

Answer: 1. Semantic Network Representation

Explanation:

• Semantic Network Representation is an alternative to predicate logic for knowledge representation.
• It uses nodes and links to represent relationships between concepts.

Answer: 4. Production Rules

Explanation:

• Production Rules consist of < condition, action > pairs.
• These rules are used to represent knowledge in a way that can be easily applied to solve problems.

Answer: 3. Frame Representation

Explanation:

• Frame Representation is a knowledge representation technique where knowledge is represented as a set of objects and their attributes.
• It is similar to object-oriented programming.

Answer: 1. Semantic Network Representation

Explanation:

• Semantic Network Representation uses a graph of nodes and links to represent knowledge.
• Nodes represent concepts, and links represent relationships between them.

Answer: 4. Production Rules

Explanation:

• Production Rules represent knowledge as a set of rules.
• These rules are used to infer new knowledge from existing knowledge.

Answer: 2. Logical Representation

Explanation:

• Logical Representation uses logical statements to represent knowledge.
• It is based on formal logic and provides a precise way to represent information.

Answer: 3. Frame Representation

Explanation:

• Frame Representation uses frames to represent knowledge.
• Each frame contains slots that represent attributes of an object or concept.

Answer: 4. Production Rules

Explanation:

• Production Rules represent knowledge as a set of if-then rules.
• These rules are used to infer new knowledge from existing knowledge.

Answer: 1. Semantic Network Representation

Explanation:

• Semantic Network Representation uses nodes and links to represent knowledge.
• Nodes represent concepts, and links represent relationships between them.

Answer: 2. Logical Representation

Explanation:

• Logical Representation uses logical statements to represent knowledge.
• It is based on formal logic and provides a precise way to represent information.

Answer: 3. Frame Representation

Explanation:

• Frame Representation uses frames to represent knowledge.
• Each frame contains slots that represent attributes of an object or concept.

Answer: 4. Production Rules

Explanation:

• Production Rules represent knowledge as a set of if-then rules.
• These rules are used to infer new knowledge from existing knowledge.