Experiments in search and knowledge
Experiments in search and knowledge
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
Parallel Search of Strongly Ordered Game Trees
ACM Computing Surveys (CSUR)
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Move Ordering Using Neural Networks
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Multi-cut Pruning in Alpha-Beta Search
CG '98 Proceedings of the First International Conference on Computers and Games
The principal continuation and the killer heuristic
ACM '77 Proceedings of the 1977 annual conference
Information Sciences: an International Journal
Enhancements for multi-player Monte-Carlo tree search
CG'10 Proceedings of the 7th international conference on Computers and games
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In this paper a new method is described for move ordering, called the relative history heuristic. It is a combination of the history heuristic and the butterfly heuristic. Instead of only recording moves which are the best move in a node, we also record the moves which are applied in the search tree. Both scores are taken into account in the relative history heuristic. In this way we favour moves which on average are good over moves which are sometimes best. Experiments in LOA show that our method gives a reduction between 10 and 15 per cent of the number of nodes searched. Preliminary experiments in Go confirm this result. The relative history heuristic seems to be a valuable element in move ordering.