2021/08/13 by Jorge Fandinno, François Laferrière, Fandinno, Jorge +7
Computer Science · #Artificial Intelligence (cs.AI) #D.1.6 #FOS: Computer and information sciences #Formal Methods in Verification #Logic, Reasoning, and Knowledge #Multi-Agent Systems and Negotiation
paper · pdf · doi:10.48550/arxiv.2108.06405
openalex publication_date 2021/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
We present a general approach to planning with incomplete information in\nAnswer Set Programming (ASP). More precisely, we consider the problems of\nconformant and conditional planning with sensing actions and assumptions. We\nrepresent planning problems using a simple formalism where logic programs\ndescribe the transition function between states, the initial states and the\ngoal states. For solving planning problems, we use Quantified Answer Set\nProgramming (QASP), an extension of ASP with existential and universal\nquantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs).\nWe define the language of quantified logic programs and use it to represent the\nsolutions to different variants of conformant and conditional planning. On the\npractical side, we present a translation-based QASP solver that converts\nquantified logic programs into QBFs and then executes a QBF solver, and we\nevaluate experimentally the approach on conformant and conditional planning\nbenchmarks. Under consideration for acceptance in TPLP.\n