Adversarial Search

What is an adversarial search problem?

• A search problem that involves finding a path from a start state to a goal state
• A search problem that involves finding the optimal solution
• A search problem that involves multiple agents that are trying to outsmart each other
• Adversarial search problems are not used in AI

Which algorithm is commonly used for solving adversarial search problems?

• Depth-first search
• Breadth-first search
• A* search
• Minimax

What is the purpose of the evaluation function in adversarial search algorithms?

• To evaluate the quality of a move
• To evaluate the utility of a state
• To evaluate the performance of the algorithm
• The evaluation function is not used in adversarial search algorithms

What is alpha-beta pruning?

• An algorithm that eliminates nodes from the search tree that are guaranteed to be worse than the current best option
• An algorithm that eliminates nodes from the search tree that are guaranteed to be better than the current best option
• An algorithm that eliminates nodes from the search tree that are not visited frequently enough
• Alpha-beta pruning is not used in adversarial search

What is the difference between minimax and alpha-beta pruning?

• Minimax is a deterministic algorithm, while alpha-beta pruning is a stochastic algorithm
• Minimax always explores the entire search tree, while alpha-beta pruning can eliminate parts of the tree
• Minimax is only used for two-player games, while alpha-beta pruning can be used for any adversarial search problem
• Minimax and alpha-beta pruning are the same algorithm

Which of the following games can be solved using adversarial search algorithms?

• Tic-Tac-Toe
• Checkers
• Chess
• All of the above

In minimax algorithm, which player's move is maximized and which player's move is minimized?

• The maximizing player tries to maximize the score, while the minimizing player tries to minimize the score
• The maximizing player tries to minimize the score, while the minimizing player tries to maximize the score
• Both players try to maximize the score
• Both players try to minimize the score

What is the difference between perfect and imperfect information games?

• Perfect information games have complete knowledge of the game state, while imperfect information games do not
• Perfect information games are deterministic, while imperfect information games are stochastic
• Perfect information games have a finite number of states, while imperfect information games do not
• Perfect information games and imperfect information games are the same