DIFFOBJ — A Game for Exercising Teams of Agents

  • Tony Hirst
  • Tony Kalus
Conference paper


Despite the increasing amount of research in the use of theories of teamwork as an organising principle for multi-agent systems, there are few, if any, simple standard problems for testing such systems. Afetr describing the essential characteristics of a team of agents, we present one such test problem — DIFFOBJ — in which the members of a team must each collect a different object. At least initially, the particular object each individual should collect is left open. After describing DIFFOBJ, we demonstrate how it revealed certain weaknesses in a pre-existing model of teamwork for the Soar architecture. Finally, we show how DIFFOBJ tests those characteristics identified as being essential properties of individual agents who are also team members.


Team Member Team Leader Belief State Simple Game Team Plan 
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  1. [1??]
    Cohen, P. R., Levesque, H. R., & Smith, I., 1999. On team formation. In Hintikka, J. and Tuomela, R. (Eds.) Contemporary Action Theory. Synthese.Google Scholar
  2. [2]
    Hirst, A.J. & Kalus, A., in prep., “An Introduction to Teams of Agents”.Google Scholar
  3. [3]
    Tambe, M., 1997, Agent architectures for flexible, practical teamwork. In Proceedings of the National Conference on Artificial Intelligence, pages 198-202. AAAI Press.Google Scholar
  4. [4]
    Hirst, A.J. & Kalus, A., 1999, ‘Flexible Communication within Agent Teams’, to be presented at IJCAI99 Workshop on Team Behaviour, Stockholm, August 1999.Google Scholar
  5. [5]
    Newell, A., 1990, Unified Theories of Cognition. Harvard University Press, Cambridge Massachusetts.Google Scholar
  6. [6]
    Rosenbloom, P.S., Laird, J.E., Newell, A. and McCarl, R., 1991, A Preliminary Analysis of the Soar Architecture as a Basis for General Intelligence. Artificial Intelligence (47).Google Scholar
  7. [7]
    Kalus, A. & Hirst, A.J., 1998, “Soar Agents for OOTW Mission Simulation. “ Presented at Command & Control Decision Making in Emerging Conflicts — 4th International Command & Control Research & Technology Symposium, Näsby Park, Sweden.Google Scholar
  8. [8]
    Smith, I.A. & Cohen, P.R., 1996. Towards Semantics for an agent communication language based on speech acts. In Proceedings of the National Conference on Artificial Intelligence (AAAI′96), pages 24–31, Menlo Park, California. AAAI Press.Google Scholar

Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Tony Hirst
    • 1
  • Tony Kalus
    • 1
  1. 1.Intelligent Agent Group, Department of Computer ScienceUniversity of PortsmouthHampshire Terrace, PortsmouthUK

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