Planning with Incomplete Information in Quantified Answer Set Programming

TL;DR

This paper introduces a novel approach for planning under incomplete information using Quantified Answer Set Programming, enabling the representation and solving of conformant and conditional planning problems with sensing actions.

Contribution

It extends ASP with quantifiers to handle incomplete information and provides a translation-based QASP solver that converts problems into QBFs for solving.

Findings

Effective representation of planning problems with incomplete information.

Successful experimental evaluation on planning benchmarks.

Demonstrates the feasibility of QASP for complex planning tasks.

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 to 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. Under consideration for acceptance in TPLP.