# Answer Set Planning: A Survey

arXiv: https://arxiv.org/abs/2202.05793 · HTML: https://mlsanity.com/paper/2202.05793/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 276 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 30 of 30)

- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [ASP-Core-2 Input Language Format](https://mlsanity.com/paper/1911.04326/) (2019)
- [Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems](https://mlsanity.com/paper/1804.08229/) (2018)
- [Optimal Target Assignment and Path Finding for Teams of Agents](https://mlsanity.com/paper/1612.05693/) (2016)
- [Multi-Agent Path Finding with Delay Probabilities](https://mlsanity.com/paper/1612.05309/) (2016)
- [MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees](https://mlsanity.com/paper/1401.3905/) (2014)
- [Clingcon: The Next Generation](https://mlsanity.com/paper/1705.04569/) (2017)
- [Topological Value Iteration Algorithms](https://mlsanity.com/paper/1401.3910/) (2014)
- [Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and Effective Heuristics](https://mlsanity.com/paper/1507.03290/) (2015)
- [Experimenting with robotic intra-logistics domains](https://mlsanity.com/paper/1804.10247/) (2018)
- [Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width](https://mlsanity.com/paper/1401.3468/) (2014)
- [A General Multi-agent Epistemic Planner Based on Higher-order Belief Change](https://mlsanity.com/paper/1806.11298/) (2018)
- [Cooperative Epistemic Multi-Agent Planning for Implicit Coordination](https://mlsanity.com/paper/1703.02196/) (2017)
- [Planning with Incomplete Information in Quantified Answer Set Programming](https://mlsanity.com/paper/2108.06405/) (2021)
- [Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming](https://mlsanity.com/paper/1804.10437/) (2018)
- [Multi-Agent Path Finding with Deadlines](https://mlsanity.com/paper/1806.04216/) (2018)
- [plasp 3: Towards Effective ASP Planning](https://mlsanity.com/paper/1812.04491/) (2018)
- [Hybrid Conditional Planning using Answer Set Programming](https://mlsanity.com/paper/1707.05904/) (2017)
- [Modelling Multi-Agent Epistemic Planning in ASP](https://mlsanity.com/paper/2008.03007/) (2020)
- [Lazy Model Expansion: Interleaving Grounding with Search](https://mlsanity.com/paper/1402.6889/) (2014)
- [Multi-Agent Pathfinding with Continuous Time](https://mlsanity.com/paper/1901.05506/) (2019)
- [Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach](https://mlsanity.com/paper/2008.03496/) (2020)
- [An approach to multi-agent planning with incomplete information](https://mlsanity.com/paper/1501.07256/) (2015)
- [Degrees of Laziness in Grounding: Effects of Lazy-Grounding Strategies on ASP Solving](https://mlsanity.com/paper/1903.12510/) (2019)
- [Supply Restoration in Power Distribution Systems - A Case Study in Integrating Model-Based Diagnosis and Repair Planning](https://mlsanity.com/paper/1302.3608/) (2013)
- [Solving Distributed Constraint Optimization Problems Using Logic Programming](https://mlsanity.com/paper/1705.03916/) (2017)
- [Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+](https://mlsanity.com/paper/1904.00512/) (2019)
- [A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains](https://mlsanity.com/paper/1905.07028/) (2019)

## Built on by (most cited first, 12 of 12)

- [NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains](https://mlsanity.com/paper/2503.00870/) (2025)
- [CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning](https://mlsanity.com/paper/2406.03367/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Autonomous Agents and Policy Compliance: A Framework for Reasoning About Penalties](https://mlsanity.com/paper/2512.03931/) (2025)
- [Planning as Theorem Proving with Heuristics](https://mlsanity.com/paper/2303.13638/) (2023)
- [Rethinking Answer Set Programming Templates](https://mlsanity.com/paper/2307.06382/) (2023)
- [Computing Universal Plans for Partially Observable Multi-Agent Routing Using Answer Set Programming](https://mlsanity.com/paper/2305.16203/) (2023)
- [What killed the cat? Towards a logical formalization of curiosity (and suspense, and surprise) in narratives](https://mlsanity.com/paper/2410.08597/) (2024)
- [Fine-grained Timing Analysis of Digital Integrated Circuits in Answer Set Programming](https://mlsanity.com/paper/2507.11150/) (2025)
- [Long-term Power Grid Planning via Answer Set Programming](https://mlsanity.com/paper/2605.20172/) (2026)
- [2-ASP(Q) programs with weak constraints: Complexity and efficient implementation](https://mlsanity.com/paper/2605.27338/) (2026)
- [Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints](https://mlsanity.com/paper/2608.13684/) (2026)

## Abstract (arXiv)

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, i.e., solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.