Balancing local resources and global goals in multiply-constrained DCOP

  • Authors:

  • Emma Bowring;Milind Tambe;Makoto Yokoo

  • Affiliations:

  • (Correspd. E-mail: ebowring@pacific.edu) Computer Science Department, University of the Pacific, Stockton, CA 95211, USA;Computer Science Department, University of Southern California, Los Angeles, CA 90089, USA;Computer Science Department, Kyushu University, Fukuoka, 819-0395, Japan

  • Venue:
  • Multiagent and Grid Systems
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. Yet, these resource constraints may need to be kept private. Designing DCOP algorithms for these domains requires managing complex trade-offs in completeness, scalability, privacy and efficiency. This article defines the multiply-constrained DCOP (MC-DCOP) framework and provides complete (globally optimal) and incomplete (locally optimal) algorithms for solving MC-DCOP problems. Complete algorithms find the best allocation of scarce resources while optimizing the team objective, while incomplete algorithms are more scalable. The algorithms use four main techniques: (i) transforming constraints to maintain privacy; (ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds; and (iv) using a virtual assignment to flag problems rendered unsatisfiable by resource constraints. Proofs of correctness are presented for all algorithms. Experimental results illustrate the strengths and weaknesses of both the complete and incomplete algorithms.