Cost sensitive reachability heuristics for handling state uncertainty

Daniel Bryce, Subbarao Kambhampati

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scalability. On the other hand, non-deterministic conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with non-uniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques.

Original languageEnglish (US)
Title of host publicationProceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
Pages60-68
Number of pages9
StatePublished - Dec 1 2005
Event21st Conference on Uncertainty in Artificial Intelligence, UAI 2005 - Edinburgh, United Kingdom
Duration: Jul 26 2005Jul 29 2005

Publication series

NameProceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005

Other

Other21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
CountryUnited Kingdom
CityEdinburgh
Period7/26/057/29/05

ASJC Scopus subject areas

  • Artificial Intelligence

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    Bryce, D., & Kambhampati, S. (2005). Cost sensitive reachability heuristics for handling state uncertainty. In Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005 (pp. 60-68). (Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005).