Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions

Jingwei Chen, Robert C. Holte, Sandra Zilles, Nathan R. Sturtevant

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

Main track. Pages 489-495.

https://doi.org/10.24963/ijcai.2017/69

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Abstract

It is well-known that any admissible unidirectional heuristic search algorithm must expand all states whose f-value is smaller than the optimal solution cost when using a consistent heuristic. Such states are called “surely expanded” (s.e.). A recent study characterized s.e. pairs of states for bidirectional search with consistent heuristics: if a pair of states is s.e. then at least one of the two states must be expanded. This paper derives a lower bound, VC, on the minimum number of expansions required to cover all s.e. pairs, and present a new admissible front-to-end bidirectional heuristic search algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no more than 2VC expansions. We further prove that no admissible front-to-end algorithm has a worst case better than 2VC. Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well.

Keywords

  • Combinatorial & Heuristic Search: Combinatorial search/optimisation
  • Combinatorial & Heuristic Search: Heuristic Search
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