Replanning in domains with partial information and sensing actions

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions. At each step we generate a candidate plan which solves a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the T0 translation, in which the classical state captures the knowledge state of the agent. We overcome the non-determinism in sensing actions, and the large domain size introduced by T0 by using state sampling. Our planner also employs a novel, lazy, regression-based method for querying the belief state.

Original languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages2021-2026
Number of pages6
DOIs
StatePublished - 1 Dec 2011
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia, Spain
Duration: 16 Jul 201122 Jul 2011

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Country/TerritorySpain
CityBarcelona, Catalonia
Period16/07/1122/07/11

Fingerprint

Dive into the research topics of 'Replanning in domains with partial information and sensing actions'. Together they form a unique fingerprint.

Cite this