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

2025-02-14

Adaptive Agents and Multi-Agent Systems

AI Summary

The Coalition Structure Generation (CSG) problem in large-scale multi-agent systems—critical for real-time applications such as traffic scheduling and disaster response—requires rapidly partitioning hundreds to thousands of agents to maximize social welfare. To address this, we propose SALDAE, the first CSG algorithm formulated as a multi-agent pathfinding problem over the coalition structure graph, supporting anytime execution: it can be terminated at any time while guaranteeing monotonic improvement in solution quality. SALDAE unifies diverse heuristic functions, graph search strategies, and distributed path planning within a single scalable framework. Evaluated across nine standard value distributions—including benchmarks modeling disaster response and electric vehicle allocation—SALDAE consistently outperforms state-of-the-art methods, delivering high-quality solutions significantly faster and demonstrating superior scalability and solution quality.

Technology Category

• Multiagent Systems: Multiagent Planning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• Optimize agent partitioning into coalitions
• Enhance multiagent systems' social welfare
• Handle large-scale problems efficiently

Innovation

• Multiagent path finding algorithm
• Graph-based coalition structure search
• Anytime algorithm scalability