Revisiting Suboptimal Search
Chen, Jingwei (University of Alberta) | Sturtevant, Nathan R. (University of Alberta) | Doyle, William (University of New Hampshire) | Ruml, Wheeler (University of New Hampshire)
Suboptimal search algorithms can often solve much larger problems than optimal search algorithms, and thus have broad practical use. This paper returns to early algorithms like WA*, A*_e and Optimistic search. It studies the commonalities between these approaches in order to build a new bounded-suboptimal algorithm. Combined with recent research on avoiding node re-expansions in bounded-optimal search, a new solution quality bound is developed, which often provides proof of the solution bound much earlier during the search. Put together, these ideas provide a new state-of-the-art in bounded-optimal search.
Jul-11-2019