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Title: Planning with Incomplete Information in Quantified Answer Set Programming
Abstract We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.  more » « less
Award ID(s):
1914635
NSF-PAR ID:
10311106
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Theory and Practice of Logic Programming
Volume:
21
Issue:
5
ISSN:
1471-0684
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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