A translation based approach to probabilistic conformant planning

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3 Scopus citations

Abstract

In conformant probabilistic planning (CPP), we are given a set of actions with stochastic effects, a distribution over initial states, a goal condition, and a value 0 < p ≤1. Our task is to find a plan π such that the probability that the goal condition holds following the execution of π in the initial state is at least p. In this paper we focus on the problem of CPP with deterministic actions. Motivated by the success of the translation-based approach of Palacious and Geffner [6], we show how deterministic CPP can be reduced to a metric-planning problem. Given a CPP, our planner generates a metric planning problem that contains additional variables. These variables represent the probability of certain facts. Standard actions are modified to update these values so that this semantics of the value of variables is maintained. An empirical evaluation of our planner, comparing it to the best current CPP solver, Probabilistic-FF, shows that it is a promising approach.

Original languageEnglish
Title of host publicationAlgorithmic Decision Theory - Second International Conference, ADT 2011, Proceedings
Pages16-27
Number of pages12
DOIs
StatePublished - 31 Oct 2011
Event2nd International Conference on Algorithmic Decision Theory, ADT 2011 - Piscataway, NJ, United States
Duration: 26 Oct 201128 Oct 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6992 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Algorithmic Decision Theory, ADT 2011
Country/TerritoryUnited States
CityPiscataway, NJ
Period26/10/1128/10/11

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