Adversarial Search and CSP Quiz

What is the main objective of adversarial search in game theory?

• To minimize the opponent's score

• To maximize a player's score while minimizing the opponent's advantage

• To predict future game states

• To increase randomness in decision-making

In the Minimax algorithm, the 'min' player tries to:

• Maximize the opponent's score

• Minimize their own score

• Minimize the opponent's score

• Maximize their own score

Which type of games is the Minimax algorithm most commonly used for?

• Single-player games

• Deterministic and two-player games

• Stochastic games

• Multi-agent decision-making games

What is the primary assumption in the Minimax algorithm?

• Players make random moves

• Opponent always plays optimally

• Game has multiple optimal solutions

• Players cooperate for mutual benefit

The complexity of the Minimax algorithm is:

• O(log n)

• O(n)

• O(b^d)

• O(n^2)

In Minimax, the depth of the search tree is:

• Determined by the branching factor

• Always infinite

• Unimportant for evaluation

• Never a factor

Alpha-Beta pruning improves Minimax by:

• Reducing the number of nodes evaluated

• Increasing the depth of search

• Randomly selecting nodes

• Ignoring terminal states

What is the best-case time complexity of Alpha-Beta pruning?

• O(b^d)

• O(b^(d/2))

• O(d^b)

• O(log b)

Alpha and Beta values in Alpha-Beta pruning represent:

• The minimum and maximum score values found so far

• Randomly assigned thresholds

• Values determined at the end of the game

• The probability of winning

Which of the following is an example of a CSP?

• Chess

• Sudoku

• Stock market prediction

• Image recognition