Generalized 3-Valued Belief States in Conformant Planning

Jussi Rintanen, Saurabh Fadnis

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

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The high complexity of planning with partial observability has motivated to find compact representations of belief state (sets of states) that reduce their size exponentially, including the 3-valued literal-based approximations by Baral et al. and tag-based approximations by Palacios and Geffner. We present a generalization of 3-valued literal-based approximations, and an algorithm that analyzes a succinctly represented planning problem to derive a set of formulas the truth of which accurately represents any reachable belief state. This set is not limited to literals and can contain arbitrary formulas. We demonstrate that a factored representation of belief states based on this analysis enables fully automated reduction of conformant planning problems to classical planning, bypassing some of the limitations of earlier approaches.

Original languageEnglish
Title of host publicationPRICAI 2022: Trends in Artificial Intelligence - 19th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2022, Shanghai, China, November 10-13, 2022, Proceedings
EditorsSankalp Khanna, Jian Cao, Quan Bai, Guandong Xu
ISBN (Electronic)978-3-031-20862-1
ISBN (Print)978-3-031-20861-4
Publication statusPublished - Nov 2022
MoE publication typeA4 Conference publication
EventPacific Rim International Conference on Artificial Intelligence - Shanghai, China
Duration: 10 Nov 202213 Nov 2022
Conference number: 19

Publication series

NameLecture Notes in Computer Science
Volume13629 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific Rim International Conference on Artificial Intelligence
Abbreviated titlePRICAI


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