BIDA: an improved perimeter search algorithm
Artificial Intelligence
Single-agent search in the presence of deadlocks
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Searching with Pattern Databases
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Sokoban: Evaluating Standard Single-Agent Search Techniques in the Presence of Deadlock
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Unifying Single-Agent and Two-Player Search
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Plausible Move Generation Using Move Merit Analysis with Cut-Off Thresholds in Shogi
CG '00 Revised Papers from the Second International Conference on Computers and Games
Inconsistent heuristics in theory and practice
Artificial Intelligence
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AI research has developed an extensive collection of methods to solve state-space problems. Using the challenging domain of Sokoban, this paper studies the effect of search enhancements on program performance. We show that the current state of the art in AT generally requires a large programming and research effort into domain-dependent: methods to solve even moderately complex problems in such difficult domains. The application of domain-specific knowledge to exploit properties of the search space can result in large reductions in the size of the search tree, often several orders of magnitude per search enhancement. Understanding the effect of these enhancements on the search leads to a new taxonomy of search enhancements, and a new framework for developing single-agent search applications. This is used to illustrate the large gap between what is portrayed in the literature versus what is needed in practice.