Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks

  • Avi Rosenfeld
  • Sarit Kraus
  • Charlie Ortiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4676)


In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no polynomial algorithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a general approach where local agents can estimate their problem tightness, or how constrained their local subproblem is. This allows these agents to immediately identify many problems which are not constrained, and will not benefit from sending or receiving further information. Next, agents use traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained problems will most benefit from human attention. We evaluated this approach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brueckner, S.A., Van Dyke Parunak, H.: Resource-aware exploration of the emergent dynamics of simulated systems. In: Sven, A. (ed.) AAMAS 2003. Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 781–788. ACM Press, New York (2003)CrossRefGoogle Scholar
  2. 2.
    Cheeseman, P., Kanefsky, B., Taylor, W.M.: Where the Really Hard Problems Are. In: IJCAI 1991. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, Sidney, Australia, pp. 331–337 (1991)Google Scholar
  3. 3.
    Domingos, P.: Metacost: a general method for making classifiers cost-sensitive. In: KDD 1999. Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 155–164. ACM Press, New York (1999)CrossRefGoogle Scholar
  4. 4.
    Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Horling, B., Neiman, D., Podorozhny, R., NagendraPrasad, M., Raja, A., Vincent, R., Xuan, P., Zhang, X.Q.: Evolution of the GPGP/TAEMS Domain-Independent Coordination Framework. Autonomous Agents and Multi-Agent Systems 9(1), 87–143 (2004)CrossRefGoogle Scholar
  5. 5.
    Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, pp. 438–445. Springer, Heidelberg (2005)Google Scholar
  6. 6.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  7. 7.
    Monasson, R., Zecchina, R., Kirkpatrick, S., Selman, B., Troyansky, L.: Determining computational complexity from characteristic “phase transitions”. Nature 400(6740), 133–137 (1999)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Rosenfeld, A.: A study of dynamic coordination mechanisms. Ph.D. Dissertation, Bar Ilan University (2007)Google Scholar
  9. 9.
    Shen, J., Becker, R., Lesser, V.: Agent Interaction in Distributed MDPs and its Implications on Complexity. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems, Japan, pp. 529–536. ACM, New York (2006)CrossRefGoogle Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. In (Morgan Kaufmann Series in Data Management Systems), 2nd edn., Morgan Kaufmann, San Francisco (2005)Google Scholar
  11. 11.
    Maheswaran, R.T., Tambe, M., Bowring, E., Pearce, J.P., Varakantham, P.: Taking dcop to the real world: Efficient complete solutions for distributed multi-event scheduling. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, pp. 310–317. Springer, Heidelberg (2005)Google Scholar
  12. 12.
    Sarne, D., Grosz, B.: Estimating Information Value in Collaborative Multi-Agent Planning Systems. In: AAMAS 2007 (2007)Google Scholar
  13. 13.
    Scerri, P., Pynadath, D.V., Tambe, M.: Towards adjustable autonomy for the real world. In: JAIR, vol. 17, pp. 171–228 (2002)Google Scholar
  14. 14.
    Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: The distributed constraint satisfaction problem: Formalization and algorithms. Knowledge and Data Engineering 10(5), 673–685 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Avi Rosenfeld
    • 1
    • 2
  • Sarit Kraus
    • 2
  • Charlie Ortiz
    • 3
  1. 1.Department of Industrial Engineering, Jerusalem College of Technology, JerusalemIsrael
  2. 2.Department of Computer Science Bar Ilan University, Ramat GanIsrael
  3. 3.SRI International, 333 Ravenswood Avenue Menlo Park, CA 94025-3493USA

Personalised recommendations