The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
New advances in Alpha-Beta searching
CSC '96 Proceedings of the 1996 ACM 24th annual conference on Computer science
Unifying single-agent and two-player search
Information Sciences: an International Journal - Heuristic Search and Computer Game Playing
Machine learning in games: a survey
Machines that learn to play games
The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
Selective depth-first game-tree search
Selective depth-first game-tree search
Adaptive parallel iterative deepening search
Journal of Artificial Intelligence Research
Temporal difference learning applied to a high-performance game-playing program
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Evolving board-game players with genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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A plethora of enhancements are available to be used together with the αβ search algorithm. There are so many, that their selection and implementation is a non-trivial task, even for the expert. Every domain has its specifics which affect the search tree Even seemingly minute changes to an evaluation function can have an impact on the characteristics of a search tree. In turn, different tree characteristics must be addressed by selecting different enhancements. This paper introduces Pilot, a system for automatically selecting enhancements for αβ search. Pilot generates its own test data and then uses a greedy search to explore the space of possible enhancements. Experiments with multiple domains show differing enhancement selections. Tournament results are presented for two games to demonstrate that automatically generated αβ search performs at least on a par with what is achievable by hand-crafted search engines, but with orders of magnitude less effort in its creation.