Best-first fixed-depth minimax algorithms
Artificial Intelligence
Benefits of using multivalued functions for minimaxing
Artificial Intelligence
Improving state evaluation, inference, and search in trick-based card games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Exploiting graph properties of game trees
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Improving state evaluation, inference, and search in trick-based card games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Alpha-Beta is the most common game tree search algorithm, due to its high-performance and straightforward implementation. In practice one must find the best trade-off between heuristic evaluation time and bringing the subset of nodes explored closer to a minimum proof graph. In this paper we present a series of structural properties of minimum proof graphs that help us to prove that finding such graphs is NP-hard for arbitrary DAG inputs, but can be done in linear time for trees. We then introduce the class of fastest-cut-first search heuristics that aim to approximate minimum proof graphs by sorting moves based on approximations of sub-DAG values and sizes. To explore how various aspects of the game tree (such as branching factor and distribution of move values) affect the performance of Alpha-Beta we introduce the class of "Prefix Value Game Trees" that allows us to label interior nodes with true minimax values on the fly without search. Using these trees we show that by explicitly attempting to approximate a minimum game tree we are able to achieve performance gains over Alpha-Beta with common extensions.