set-4

151. Main features of hill climbing algorithm is ______.

1. Generate and test variant

2. No back tracking

3. Greedy approach

4. All of the above

Show me the answer

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.

152. ______ is the problem in Hill climbing searching.

1. Local Maximum

2. Plateau

3. Ridges

4. All of mentioned above

Show me the answer

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.

153. In ______ we escape local maxima by allowing some "bad" moves but gradually decrease their frequency.

1. Hill Climbing

2. Simulated Annealing

3. Mini max

4. Alpha beta pruning

Show me the answer

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.

154. Simulated Annealing is ______ optimization technique.

1. Local

2. Global

3. Both Local and Global

4. None of above

Show me the answer

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.

155. Simulated Annealing is a ______ algorithm, the algorithm does not use any information gathered during the search.

1. Memory equipped

2. Memory less

3. Processor equipped

4. None of above

Show me the answer

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.

156. The process of annealing can be simulated with the metropolis algorithm which is based on ______ techniques.

1. Closed form solution

2. Monte-Carlo simulation

3. Numerical Transformation Method

4. Proposed Method

Show me the answer

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.

157. Adversarial Search uses ______ environment

1. Collective

2. Competitive

3. Cooperative

4. Both Collective and Cooperative

Show me the answer

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.

158. General game involves ______ agents

1. Multi

2. Single

3. Only single and multi

4. Neither single nor multi

Show me the answer

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.

159. ______ search methods only consider how close the agent is to the goal state.

1. Multi-agent

2. Single-agent

3. Both single and multi-agent

4. None of above

Show me the answer

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.

160. In ______ games, decisions of both agents have to be taken into account: a decision made by one agent will affect the resulting search space that the other agent would need to explore.

1. Single player

2. Two-player

3. No Player

4. Both Single and Two player

Show me the answer

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.

161. To formalize a two-player game as a search problem an agent can be called ______ and the opponent can be called ______.

1. MINI, MAX

2. MAX, MIN

3. MIN, MIN

4. MAX, MAX

Show me the answer

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.

162. MINI MAX Algorithm is perfect for deterministic and is a ______ game.

1. Single player (Computer)

2. Two-player (Computer and User)

3. Single player (User)

4. None of above

Show me the answer

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.

163. Free cell, 8-puzzle, Rubrik’s cube is an example of ______.

1. Deterministic Multi player

2. Deterministic Single player

3. Non-Deterministic single player

4. Non-deterministic multi player

Show me the answer

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.

164. Tic-tac-toe, Chess, Checker is a example of ______.

1. Deterministic Multi player

2. Deterministic Single player

3. Non-Deterministic single player

4. Non-deterministic multi player

Show me the answer

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.

165. In ______ game, one player maximize result, another player minimize result.

1. Zero-player

2. One-player

3. Two-player

4. Thee-player

Show me the answer

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.

166. ______ is the time and ______ is the space complexity of MINI MAX Algorithm.

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

2. $O(b^{m+1})$, $O(bm)$

3. $O(b^m)$, $O(b^m)$

4. $O(bm)$, $O(b^m)$

Show me the answer

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.

167. The minimax algorithm performs a complete ______ exploration of the game tree.

1. Breadth-first

2. Depth-first

3. Best-first

4. None of above

Show me the answer

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.

168. Is MINI MAX Search complete if tree is finite?

1. May be

2. No

3. Yes

4. Rather not say

Show me the answer

Answer: 3. Yes

Explanation:

• Minimax Search is complete if the game tree is finite.

• It will eventually find the optimal solution if one exists.

169. For making decision of win/lose, we apply ______ algorithm on game tree.

1. Greedy search Algorithm

2. Hill Climbing Algorithm

3. Mini Max Algorithm

4. BFS/DFS Algorithm

Show me the answer

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.

170. In Alpha-Beta Pruning Algorithm, Pruning ______ the final result.

1. Might affect

2. Does not affect

3. Affect

4. Sometime affects, sometime doesn’t affect

Show me the answer

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.

171. Why it is called Alpha-Beta? α is the value of the best ______ choice found so far at any choice point along the path for max.

1. Lowest value

2. Average value

3. Highest value

4. Infinite value

Show me the answer

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.

172. ______ is a modified version of the Mini Max Algorithm.

1. Hill climbing

2. Alpha beta pruning

3. BFS

4. DFS

Show me the answer

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.

173. To ______ depth does the alpha-beta pruning can be applied.

1. 12 states

2. 5 States

3. 1 States

4. Any depth

Show me the answer

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.

174. In alpha-beta pruning, the initial value of alpha is ______ and beta is ______.

1. Negative Infinity, Positive Infinity

2. -1, +1

3. Positive Infinity, Negative Infinity

4. +1, -1

Show me the answer

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.

175. The main condition which required for alpha-beta pruning is?

1. alpha>=beta

2. alpha=beta

3. alpha<=bet

4. alpha!=beta

Show me the answer

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.

176. The 2 types of move ordering in Alpha-Beta Pruning are ______ and ______.

1. Best ordering, Ideal Ordering

2. Worst ordering, Ideal Ordering

3. Best ordering, Random Ordering

4. Worst ordering, Random Ordering

Show me the answer

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.

177. In Alpha-Beta pruning, With "perfect ordering," time complexity = ______.

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

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

3. $O(b^m)$

4. $O(bm)$

Show me the answer

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.

178. Identify the type of knowledge in Artificial Intelligence.

1. Procedural and Declarative Knowledge

2. Meta Knowledge

3. Structural and Heuristic Knowledge

4. All of above

Show me the answer

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.

179. ______ Knowledge is also known as Imperative Knowledge.

1. Procedural

2. Meta

3. Structural

4. Heuristic

Show me the answer

Answer: 1. Procedural

Explanation:

• Procedural Knowledge is also known as Imperative Knowledge.

• It involves knowing how to perform tasks or procedures.

180. ______ is non procedural, independent of targets and problem solving.

1. Procedural Knowledge

2. Declarative Knowledge

3. Meta Knowledge

4. Structural Knowledge

Show me the answer

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.

181. ______ is a knowledge about knowledge and how to gain them.

1. Procedural Knowledge

2. Declarative Knowledge

3. Meta Knowledge

4. Structural Knowledge

Show me the answer

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.

182. ______ represents a knowledge of some experts in a field or subject.

1. Procedural Knowledge

2. Declarative Knowledge

3. Heuristic Knowledge

4. Structural Knowledge

Show me the answer

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.

183. ______ talks about what relationship exists between concept/objects.

1. Procedural Knowledge

2. Declarative Knowledge

3. Heuristic Knowledge

4. Structural Knowledge

Show me the answer

Answer: 4. Structural Knowledge

Explanation:

• Structural Knowledge describes the relationships between concepts or objects.

• It involves understanding how different elements are connected.

184. Knowledge Representation and Reasoning represents information from the real world for a computer to understand and then utilize this knowledge to solve ______.

1. Simplest real-life problems

2. Complex real-life problems

3. Neither simplest nor complex problems

4. None of above

Show me the answer

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.

185. Different kinds of knowledge that need to be represented in AI are ______.

1. Object, Events, Performance, Facts

2. Knowledge base

3. Meta knowledge

4. All of above

Show me the answer

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.

186. ______ is the technique of knowledge representation in AI

1. Logical Representation

2. Semantic Network and Frame Representation

3. Production Rules

4. All of above

Show me the answer

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.

187. ______ is a language with some definite rules which deal with propositions & has no ambiguity in representation.

1. Semantic Network representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

188. In order to give information to agent and get info without errors in communication, we use ______ technique of knowledge representation.

1. Semantic Network representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

189. ______ work as an alternative of predicate logic for knowledge representation.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

190. ______ knowledge representation consists of < condition, action > pairs

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

191. ______ is a knowledge representation technique where knowledge is represented as a set of objects and their attributes.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

192. ______ is a knowledge representation technique where knowledge is represented as a graph of nodes and links.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

193. ______ is a knowledge representation technique where knowledge is represented as a set of rules.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

194. ______ is a knowledge representation technique where knowledge is represented as a set of logical statements.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

195. ______ is a knowledge representation technique where knowledge is represented as a set of frames.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

Answer: 3. Frame Representation

Explanation:

• Frame Representation uses frames to represent knowledge.

• Each frame contains slots that represent attributes of an object or concept.

196. ______ is a knowledge representation technique where knowledge is represented as a set of if-then rules.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

197. ______ is a knowledge representation technique where knowledge is represented as a set of nodes and links.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

198. ______ is a knowledge representation technique where knowledge is represented as a set of logical statements.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.

199. ______ is a knowledge representation technique where knowledge is represented as a set of frames.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

Answer: 3. Frame Representation

Explanation:

• Frame Representation uses frames to represent knowledge.

• Each frame contains slots that represent attributes of an object or concept.

200. ______ is a knowledge representation technique where knowledge is represented as a set of if-then rules.

1. Semantic Network Representation

2. Logical Representation

3. Frame Representation

4. Production Rules

Show me the answer

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.