# Planning with Incomplete Information in Quantified Answer Set Programming

arXiv: https://arxiv.org/abs/2108.06405 · HTML: https://mlsanity.com/paper/2108.06405/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width](https://mlsanity.com/paper/1401.3468/) (2014)
- [Temporal Answer Set Programming on Finite Traces](https://mlsanity.com/paper/1804.10227/) (2018)
- [DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL](https://mlsanity.com/paper/1702.08256/) (2017)
- [Beyond NP: Quantifying over Answer Sets](https://mlsanity.com/paper/1907.09559/) (2019)
- [Hybrid Conditional Planning using Answer Set Programming](https://mlsanity.com/paper/1707.05904/) (2017)
- [QRATPre+: Effective QBF Preprocessing via Strong Redundancy Properties](https://mlsanity.com/paper/1904.12927/) (2019)

## Built on by (most cited first, 8 of 8)

- [Answer Set Planning: A Survey](https://mlsanity.com/paper/2202.05793/) (2022)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [An efficient solver for ASP(Q)](https://mlsanity.com/paper/2305.10021/) (2023)
- [On the generalization of learned constraints for ASP solving in temporal domains](https://mlsanity.com/paper/2401.16124/) (2024)
- [Quantifying over Optimum Answer Sets](https://mlsanity.com/paper/2408.07697/) (2024)
- [The Stable Model Semantics for Higher-Order Logic Programming](https://mlsanity.com/paper/2408.10563/) (2024)
- [Using ASP(Q) to Handle Inconsistent Prioritized Data](https://mlsanity.com/paper/2604.21603/) (2026)
- [2-ASP(Q) programs with weak constraints: Complexity and efficient implementation](https://mlsanity.com/paper/2605.27338/) (2026)

## Abstract (arXiv)

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.