A Pruning Algorithm for Imperfect Information Game
نویسنده
چکیده
IMP-minimax is the analog to minimax for games with imperfect information, like card games such as bridge or poker. It computes an optimal strategy for the game if the game has a single player and a certain natural property called perfect recall. IMP-minimax is described fully in a companion paper in this proceedings. Here we introduce an algorithm IMP-alpha-beta that is to IMP-minimax s alpha-beta is to minimax. That is, IMP-alpha-beta computes the same value as IMP-minimax does, but usually faster through pruning (i.e., not examining the value of some leaves). IMP-alpha-beta includes common pruning techniques and introduces a new technique, information set pruning. We suggest a natural model in which to study the performance of search algorithms for imperfect information games and we analyze IMP-alpha-beta in the context of that model. Our analysis includes both theorems bounding the performance of IMP-alpha-beta and empirical data indicating its average-case behavior.
منابع مشابه
A Pruning Algorithm for Imperfect Information Games
IMP-minimax is the analog to minimax for games with imperfect information, like card games such as bridge or poker. It computes an optimal strategy for the game if the game has a single player and a certain natural property called perfect recall. IMP-minimax is described fully in a companion paper in this proceedings. Here we introduce an algorithm IMP-alpha-beta that is to IMP-minimax as alpha...
متن کاملBiding Strategy in Restructured Environment of Power Market Using Game Theory
In the restructured environment of electricity market, firstly the generating companies and the customers are looking for maximizing their profit and secondly independent system operator is looking for the stability of the power network and maximizing social welfare. In this paper, a one way auction in the electricity market for the generator companies is considered in both perfect and imperfec...
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 کاملPerfect Recall and Pruning in Games with Imperfect Information
Games with imperfect information are an interesting and important class of games. They include most card games (e.g., bridge and poker) as well as many economic and political models. Here we investigate algorithms for finding the simplest form of a solution (a pure-strategy equilibrium point) to imperfect information games expressed in their extensive (game tree) form. We introduce to the artif...
متن کاملPerfect Recall and Pruning in Games withImperfect
Games with imperfect information are an interesting and important class of games. They include most card games (e.g., bridge and poker), as well as many economic and political models. Here, we investigate algorithms for solving imperfect information games expressed in their extensive (game-tree) form. In particular, we consider algorithms for the simplest form of solution | a pure-strategy equi...
متن کامل