Artificial Intelligence
BIDA: an improved perimeter search algorithm
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Disjoint pattern database heuristics
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Maximizing over multiple pattern databases speeds up heuristic search
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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
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Recent progress in heuristic search: a case study of the four-peg towers of Hanoi problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
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
Partial Symbolic Pattern Databases for Optimal Sequential Planning
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Scaling Search with Pattern Databases
Model Checking and Artificial Intelligence
Multi-valued Pattern Databases
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Forward perimeter search with controlled use of memory
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Symbolic merge-and-shrink for cost-optimal planning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A pattern database abstraction (PDB) is a heuristic function in a form of a lookup table. A PDB stores the cost of optimal solutions for instances of abstract problems (subproblems). These costs are used as admissible heuristics for the original problem. Perimeter search (PS) is a form of bidirectional search. First, a breadth-first search is performed backwards from the goal state. Then, a forward search is executed towards the nodes of the perimeter. In this paper we study the effect of combining these two techniques. We describe two methods for doing this. The simplified method uses a regular PDB (towards a single goal state) but uses the perimeter to correct heuristics of nodes outside the perimeter. The second, more advanced method is to build a PDB that stores the cost of reaching any node of the perimeter from a given pattern. Although one might see great potential for speedup in the advanced method, we theoretically show that surprisingly most of the benefit of combining perimeter and PDBs is already exploited by the first method. We also provide experimental results that confirm our findings. We then study the behavior of our new approach when combined with methods for using multiple PDBs such as maxing and adding.