Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2012

Abstract

Multi-agent planning is a well-studied problem with various applications including disaster rescue, urban transportation and logistics, both for autonomous agents and for decision support to humans. Due to computational constraints, existing research typically focuses on one of two scenarios: unstructured domains with many agents where we are content with heuristic solutions, or domains with small numbers of agents or special structure where we can provide provably near-optimal solutions. By contrast, in this paper, we focus on providing provably near-optimal solutions for domains with large numbers of agents, by exploiting a common domain-general property: if individual agents each have limited influence on the overall solution quality, then we can take advantage of randomization and the resulting statistical concentration to show that each agent can safely plan based only on the average behavior of the other agents. To that end, we make two key contributions: (a) an algorithm, based on Lagrangian relaxation and randomized rounding, for solving multi-agent planning problems represented as large mixed-integer programs, (b) a proof of convergence of our algorithm to a near-optimal solution. We demonstrate the scalability of our approach with a large-scale illustrative theme park crowd management problem.

Keywords

Gradient Descent, Lagrangian Relaxation, Multi-Agent Systems

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

2012 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012: Proceedings, Macau, December 4-7

First Page

494

Last Page

501

ISBN

9780769548807

Identifier

10.1109/WI-IAT.2012.252

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/WI-IAT.2012.252

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