Lecture 4 Flashcards
What is adversarial search?
Adversarial search is a search method used in multi-agent environments where agents (players) have conflicting goals.
What makes adversarial search different from (un)informed search?
Adversarial search involves other players actively opposing the agent, requiring strategies that react to changing states.
What are the key components of a game environment in adversarial search?
Game environments can be deterministic or stochastic, with perfect or imperfect information.
What is the Minimax algorithm?
The Minimax algorithm is a backtracking algorithm that selects optimal moves by minimizing the opponent’s maximum payoff.
What assumption does the Minimax algorithm make about the players?
Minimax assumes both players play optimally, making the best possible decisions at every turn.
Why is Minimax considered a complete algorithm?
It explores all nodes in the game tree, ensuring it finds the optimal solution if one exists.
What is the time complexity of the Minimax algorithm?
The time complexity of Minimax is O(b^d), where b is the branching factor and d is the depth of the game tree.
Why is Minimax computationally expensive for games like chess?
Because the game tree for chess has an enormous branching factor and depth, evaluating all possible moves is computationally infeasible.
What is the purpose of alpha-beta pruning?
Alpha-beta pruning reduces the number of nodes evaluated in the Minimax algorithm by pruning branches that cannot influence the final decision.
How do the alpha and beta parameters work in alpha-beta pruning?
Alpha tracks the best value found for the MAX player, and beta tracks the best value for the MIN player.
What happens when alpha > beta during alpha-beta pruning?
When alpha > beta, the branch is pruned as it cannot influence the final decision.
How does move ordering affect the efficiency of alpha-beta pruning?
Good move ordering increases the effectiveness of pruning by reducing the number of nodes explored early.
How does alpha-beta pruning affect the time complexity of Minimax?
When pruning is optimal, the time complexity of Minimax with alpha-beta pruning becomes O(b^(d/2)).
What is the difference between a utility function and a heuristic evaluation?
A utility function assigns a numerical value to a state, while a heuristic estimates the quality of a state based on proximity to the goal.
Why are heuristics important in games with time constraints?
Heuristics allow the algorithm to make quick approximations of the best moves, which is critical in games with limited time for decision-making.
What is the horizon effect in depth-restricted Minimax?
The horizon effect occurs when important consequences of moves lie just beyond the depth limit of the search.
What is the main trade-off when restricting the depth of Minimax?
The trade-off is between computational efficiency and missing potentially optimal moves beyond the restricted depth.
How does alpha-beta pruning enable deeper searches compared to Minimax?
Alpha-beta pruning effectively doubles the search depth by reducing the number of nodes evaluated.
