Disjoint pattern database heuristics
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Experiments with Automatically Created Memory-Based Heuristics
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Memory-efficient A* heuristics for multiple sequence alignment
Eighteenth national conference on Artificial intelligence
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Space-efficient memory-based heuristics
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Structured duplicate detection in external-memory graph search
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Additive pattern database heuristics
Journal of Artificial Intelligence Research
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
Automated Creation of Pattern Database Search Heuristics
Model Checking and Artificial Intelligence
Journal of Artificial Intelligence Research
A general theory of additive state space abstractions
Journal of Artificial Intelligence Research
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
New methods for proving the impossibility to solve problems through reduction of problem spaces
Annals of Mathematics and Artificial Intelligence
Very large pattern databases for heuristic search
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Learning heuristic functions for large state spaces
Artificial Intelligence
MR-search: massively parallel heuristic search
Concurrency and Computation: Practice & Experience
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A pattern database (PDB) is a heuristic function in a form of a lookup table which stores the cost of optimal solutions for instances of subproblems. These subproblems are generated by abstracting the entire search space into a smaller space called the pattern space. Traditionally, the entire pattern space is generated and each distinct pattern has an entry in the pattern database. Recently, [10] described a method for reducing pattern database memory requirements by storing only pattern database values for a specific instant of start and goal state thus enabling larger PDBs to be used and achieving speedup in the search. We enhance their method by dynamically growing the pattern database until memory is full, thereby allowing using any size of memory. We also show that memory could be saved by storing hierarchy of PDBs. Experimental results on the large 24 sliding tile puzzle show improvements of up to a factor of 40 over previous benchmark results [8].