Leaf-Value Tables for Pruning Non-Zero-Sum Games
نویسنده
چکیده
Algorithms for pruning game trees generally rely on a game being zero-sum, in the case of alpha-beta pruning, or constant-sum, in the case of multi-player pruning algorithms such as speculative pruning. While existing algorithms can prune non-zero-sum games, pruning is much less effective than in constant-sum games. We introduce the idea of leaf-value tables, which store an enumeration of the possible leaf values in a game tree. Using these tables we are can make perfect decisions about whether or not it is possible to prune a given node in a tree. Leaf-value tables also make it easier to incorporate monotonic heuristics for increased pruning. In the 3-player perfect-information variant of Spades we are able to reduce node expansions by two orders of magnitude over the previous best zero-sum and non-zero-sum pruning techniques.
منابع مشابه
A TRANSITION FROM TWO-PERSON ZERO-SUM GAMES TO COOPERATIVE GAMES WITH FUZZY PAYOFFS
In this paper, we deal with games with fuzzy payoffs. We proved that players who are playing a zero-sum game with fuzzy payoffs against Nature are able to increase their joint payoff, and hence their individual payoffs by cooperating. It is shown that, a cooperative game with the fuzzy characteristic function can be constructed via the optimal game values of the zero-sum games with fuzzy payoff...
متن کاملEvaluations - per - move Wins
24 and close to that achieved by. Berliner and McConnell 1] deal with the diiculty in nding optimistic and pessimistic bounds on the real values of game-tree positions. However, their results are not applicable for multi-model search where we look for bounds on the sum of two functions. The sum-bounds for a multi-model search reeect the maximal possible diierence between the subjective evaluati...
متن کاملReduced Space and Faster Convergence in Imperfect-Information Games via Regret-Based Pruning
Counterfactual Regret Minimization (CFR) is the most popular iterative algorithm for solving zero-sum imperfect-information games. Regret-Based Pruning (RBP) is an improvement that allows poorly-performing actions to be temporarily pruned, thus speeding up CFR. We introduce Total RBP, a new form of RBP that reduces the space requirements of CFR as actions are pruned. We prove that in zero-sum g...
متن کاملAlpha-Beta Pruning for Games with Simultaneous Moves
Alpha-Beta pruning is one of the most powerful and fundamental MiniMax search improvements. It was designed for sequential two-player zero-sum perfect information games. In this paper we introduce an Alpha-Beta-like sound pruning method for the more general class of “stacked matrix games” that allow for simultaneous moves by both players. This is accomplished by maintaining upper and lower boun...
متن کاملReduced Space and Faster Convergence in Imperfect-Information Games via Pruning
Iterative algorithms such as Counterfactual Regret Minimization (CFR) are the most popular way to solve large zero-sum imperfect-information games. In this paper we introduce Best-Response Pruning (BRP), an improvement to iterative algorithms such as CFR that allows poorly-performing actions to be temporarily pruned. We prove that when using CFR in zero-sum games, adding BRP will asymptotically...
متن کامل