Bounded suboptimal heuristic search in linear space

Matthew Hatem, Roni Stern, Wheeler Ruml

Research output: Contribution to conferencePaperpeer-review

Abstract

It is commonly appreciated that solving search problems optimally can overrun time and memory constraints. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time and memory consumption. However, even suboptimal search can overrun memory on large problems. The conventional approach to this problem is to combine a weighted admissible heuristic with an optimal linear space algorithm, resulting in algorithms such asWeighted IDA* (wIDA*). However, wIDA* does not exploit distanceto- go estimates or inadmissible heuristics, which have recently been shown to be helpful for suboptimal search. In this paper, we present a linear space analogue of Explicit Estimation Search (EES), a recent algorithm specifically designed for bounded suboptimal search. We call our method Iterative Deepening EES (IDEES). In an empirical evaluation, we show that IDEES dramatically outperforms wIDA* on domains with non-uniform edge costs and can scale to problems that are out of reach for the original EES.

Original languageAmerican English
Pages98-104
Number of pages7
StatePublished - 1 Dec 2013
Externally publishedYes
Event6th Annual Symposium on Combinatorial Search, SoCS 2013 - Leavenworth, WA, United States
Duration: 11 Jul 201313 Jul 2013

Conference

Conference6th Annual Symposium on Combinatorial Search, SoCS 2013
Country/TerritoryUnited States
CityLeavenworth, WA
Period11/07/1313/07/13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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