Conspiracy numbers for min-max search
Artificial Intelligence
Artificial Intelligence
The PN -search algorithm: application to tsume-shogi
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Parallel randomized best-first minimax search
Artificial Intelligence
Parallel Controlled Conspiracy Number Search
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Parallel Monte-Carlo Tree Search
CG '08 Proceedings of the 6th international conference on Computers and Games
Improving depth-first PN-search: 1 + Ɛ trick
CG'06 Proceedings of the 5th international conference on Computers and games
Job-level proof-number search for connect6
CG'10 Proceedings of the 7th international conference on Computers and games
Bitboard knowledge base system and elegant search architectures for Connect6
Knowledge-Based Systems
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Proof-Number Search (PNS) is a powerful method for solving games and game positions. Over the years, the research on PNS has steadily produced new insights and techniques. With multi-core processors becoming established in the recent past, the question of parallelizing PNS has gained new urgency. This article presents a new technique called Randomized Parallel Proof-Number Search (RPPNS) for parallelizing PNS on multi-core systems with shared memory. The parallelization is based on randomizing the move selection of multiple threads, which operate on the same search tree. RPPNS is tested on a set of complex Lines-of-Action endgame positions. Experiments show that RPPNS scales well. Four directions for future research are given.