Bounded-Cost Search Using Estimates of Uncertainty

Abstract

Many planning problems are too hard to solve optimally. In bounded-cost search, one attempts to find, as quickly as possible, a plan that costs no more than a user-provided absolute cost bound. Several algorithms have been previously proposed for this setting, including Potential Search (PTS) and Bounded-cost Explicit Estimation Search (BEES). BEES attempts to improve on PTS by predicting whether nodes will lead to plans within the cost bound or not. This paper introduces a relatively simple algorithm, Expected Effort Search (XES), which uses not just point estimates but belief distributions in order to estimate the probability that a node will lead to a plan within the bound. XES's expansion order minimizes expected search time in a simplified formal model. Experimental results on standard planning and search benchmarks show that it consistently exhibits strong performance, outperforming both PTS and BEES. We also derive improved variants of BEES that can exploit belief distributions. These new methods advance the recent trend of taking advantage of uncertainty estimates in deterministic single-agent search.