IDB-ADOPT: A Depth-First Search DCOP Algorithm

  • William Yeoh
  • Ariel Felner
  • Sven Koenig
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5655)


Many agent coordination problems can be modeled as distributed constraint optimization (DCOP) problems. ADOPT is an asynchronous and distributed search algorithm that is able to solve DCOP problems optimally. In this paper, we introduce Iterative Decreasing Bound ADOPT (IDB-ADOPT), a modification of ADOPT that changes the search strategy of ADOPT from performing one best-first search to performing a series of depth-first searches. Each depth-first search is provided with a bound, initially a large integer, and returns the first solution whose cost is smaller than or equal to the bound. The bound is then reduced to the cost of this solution minus one and the process repeats. If there is no solution whose cost is smaller than or equal to the bound, it returns a cost-minimal solution. Thus, IDB-ADOPT is an anytime algorithm that solves DCOP problems with integer costs optimally. Our experimental results for graph coloring problems show that IDB-ADOPT runs faster (that is, needs fewer cycles) than ADOPT on large DCOP problems, with savings of up to one order of magnitude.


ADOPT DCOP Distributed Constraint Optimization  Distributed Search Algorithms 


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  1. 1.
    Ali, S., Koenig, S., Tambe, M.: Preprocessing techniques for accelerating the DCOP algorithm ADOPT. In: Proceedings of AAMAS, pp. 1041–1048 (2005)Google Scholar
  2. 2.
    Bowring, E., Tambe, M., Yokoo, M.: Multiply-constrained distributed constraint optimization. In: Proceedings of AAMAS, pp. 1413–1420 (2006)Google Scholar
  3. 3.
    Chechetka, A., Sycara, K.: No-commitment branch and bound search for distributed constraint optimization. In: Proceedings of AAMAS, pp. 1427–1429 (2006)Google Scholar
  4. 4.
    Davin, J., Modi, J.: Hierarchical variable ordering for multiagent agreement problems. In: Proceedings of AAMAS, pp. 1433–1435 (2006)Google Scholar
  5. 5.
    Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of A*. Journal of the Association for Computing Machinery 32(3), 505–536 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Korf, R.: Linear-space best-first search. Artificial Intelligence 62(1), 41–78 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lesser, V., Ortiz, C., Tambe, M. (eds.): Distributed Sensor Networks: A Multiagent Perspective. Kluwer, Dordrecht (2003)zbMATHGoogle Scholar
  8. 8.
    Maheswaran, R., Tambe, M., Bowring, E., Pearce, J., Varakantham, P.: Taking DCOP to the real world: Efficient complete solutions for distributed event scheduling. In: Proceedings of AAMAS, pp. 310–317 (2004)Google Scholar
  9. 9.
    Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: Proceedings of AAMAS, pp. 438–445 (2004)Google Scholar
  10. 10.
    Modi, P., Shen, W., Tambe, M., Yokoo, M.: ADOPT: Asynchronous distributed constraint optimization with quality guarantees. Artificial Intelligence 161(1-2), 149–180 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Pecora, F., Modi, J., Scerri, P.: Reasoning about and dynamically posting n-ary constraints in ADOPT. In: Proceedings of the Distributed Constraint Reasoning Workshop (2006)Google Scholar
  12. 12.
    Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: Proceedings of IJCAI, pp. 1413–1420 (2005)Google Scholar
  13. 13.
    Scerri, P., Modi, P., Tambe, M., Shen, W.: Are multiagent algorithms relevant for real hardware? A case study of distributed constraint algorithms. In: Proceedings of the ACM Symposium on Applied Computing, pp. 38–44 (2003)Google Scholar
  14. 14.
    Schurr, N., Okamoto, S., Maheswaran, R., Scerri, P., Tambe, M.: Evolution of a teamwork model. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 307–327. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  15. 15.
    Zhang, W., Korf, R.: Performance of linear-space search algorithms. Artificial Intelligence 79(2), 241–292 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Zilberstein, S.: Operational Rationality through Compilation of Anytime Algorithms. PhD thesis, Computer Science Department, University of California, Berkeley (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • William Yeoh
    • 1
  • Ariel Felner
    • 2
  • Sven Koenig
    • 1
  1. 1.Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Information Systems EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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