# Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming

arXiv: https://arxiv.org/abs/2008.03573 · HTML: https://mlsanity.com/paper/2008.03573/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies](https://mlsanity.com/paper/2111.06494/) (2021)
- [Explainable Answer-set Programming](https://mlsanity.com/paper/2308.15901/) (2023)
- [Flexible and Explainable Solutions for Multi-Agent Path Finding Problems](https://mlsanity.com/paper/2109.08299/) (2021)
- [Computing Universal Plans for Partially Observable Multi-Agent Routing Using Answer Set Programming](https://mlsanity.com/paper/2305.16203/) (2023)
- [Extending Answer Set Programming with Rational Numbers](https://mlsanity.com/paper/2312.04249/) (2023)
- [Counterfactual Explanations as Plans](https://mlsanity.com/paper/2502.09205/) (2025)
- [An XAI View on Explainable ASP: Methods, Systems, and Perspectives](https://mlsanity.com/paper/2601.14764/) (2026)

## Abstract (arXiv)

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. We consider a general version of MAPF, called mMAPF, that involves multi-modal transportation modes (e.g., due to velocity constraints) and consumption of different types of resources (e.g., batteries). The real-world applications of mMAPF require flexibility (e.g., solving variations of mMAPF) as well as explainability. Our earlier studies on mMAPF have focused on the former challenge of flexibility. In this study, we focus on the latter challenge of explainability, and introduce a method for generating explanations for queries regarding the feasibility and optimality of solutions, the nonexistence of solutions, and the observations about solutions. Our method is based on answer set programming. This paper is under consideration for acceptance in TPLP.