Lagrangian relaxation for large-scale multi-agent planning

  • Authors:

  • Geoffrey J. Gordon;Pradeep Varakantham;William Yeoh;Hoong Chuin Lau;Ajay S. Aravamudhan;Shih-Fen Cheng

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Singapore Management University, Singapore;Singapore Management University, Singapore;Singapore Management University, Singapore;Singapore Management University, Singapore;Singapore Management University, Singapore

  • Venue:
  • Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
  • Year:
  • 2012

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Abstract

Multi-agent planning is a well-studied problem with applications in various areas. Due to computational constraints, existing research typically focuses either on 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 find provably near-optimal solutions. In contrast, here we focus on provably near-optimal solutions in domains with many agents, by exploiting influence limits. 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.