# A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation

arXiv: https://arxiv.org/abs/2502.10226 · HTML: https://mlsanity.com/paper/2502.10226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding](https://mlsanity.com/paper/2010.01367/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [An Anytime Algorithm for Optimal Coalition Structure Generation](https://mlsanity.com/paper/1401.3466/) (2014)

## Built on by (most cited first, 1 of 1)

- [The Wisdom of Agent Crowds: A Human-AI Interaction Innovation Ignition Framework](https://mlsanity.com/paper/2505.06947/) (2025)

## Abstract (arXiv)

Coalition structure generation (CSG), i.e. the problem of optimally partitioning a set of agents into coalitions to maximize social welfare, is a fundamental computational problem in multiagent systems. This problem is important for many applications where small run times are necessary, including transportation and disaster response. In this paper, we develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures. Our algorithm utilizes a variety of heuristics and strategies to perform the search and guide it. It is an anytime algorithm that can handle large problems with hundreds and thousands of agents. We show empirically on nine standard value distributions, including disaster response and electric vehicle allocation benchmarks, that our algorithm enables a rapid finding of high-quality solutions and compares favorably with other state-of-the-art methods.