Planning with Incomplete Information in Quantified Answer Set Programming. Issue 5 (24th September 2021)

• Record Type: Journal Article
• Authors: FANDINNO, JORGE; LAFERRIERE, FRANCOIS; ROMERO, JAVIER; SCHAUB, TORSTEN; SON, TRAN CAO
• Is Part Of: Theory and practice of logic programming. Volume 21:Issue 5(2021)
• Page Start: 663
• Page End: 679
• Publication Date: 2021-09-24
• Subjects: answer set programming -- planning -- quantified logics; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068421000259
• Languages: English
• ISSNs: 1471-0684

Abstract

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.