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 language | American English |
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Pages | 98-104 |
Number of pages | 7 |
State | Published - 1 Dec 2013 |
Externally published | Yes |
Event | 6th Annual Symposium on Combinatorial Search, SoCS 2013 - Leavenworth, WA, United States Duration: 11 Jul 2013 → 13 Jul 2013 |
Conference
Conference | 6th Annual Symposium on Combinatorial Search, SoCS 2013 |
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Country/Territory | United States |
City | Leavenworth, WA |
Period | 11/07/13 → 13/07/13 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications