Abstract
Bidirectional search algorithms interleave two separate searches, a normal search forward from the start state, and a search backward from the goal. It is well known that adding a heuristic to unidirectional search dramatically reduces the search effort. By contrast, despite decades of research, bidirectional heuristic search has not yet had a major impact. Additionally, no comprehensive theory was ever devised to understand the nature of bidirectional heuristic search. In this paper we aim to close this gap. We first present MM, a novel bidirectional heuristic search algorithm. Unlike previous bidirectional heuristic search algorithms, MM's forward and backward searches are guaranteed to “meet in the middle”, i.e. never expand a node beyond the solution midpoint. Based on this unique attribute we present a novel framework for comparing MM, A*, and their brute-force variants. We do this by dividing the entire state space into disjoint regions based on their distance from the start and goal. This allows us to perform a comparison of these algorithms on a per region basis and identify conditions favoring each algorithm. Finally, we present experimental results that support our theoretical analysis.
Original language | English |
---|---|
Pages (from-to) | 232-266 |
Number of pages | 35 |
Journal | Artificial Intelligence |
Volume | 252 |
DOIs | |
State | Published - 1 Nov 2017 |
Keywords
- Bidirectional search
- Heuristic search
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language
- Artificial Intelligence