Advertisement

Desiderata in Agent Architectures for Coordinating Multi-Agent Systems

  • Jaeho Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1599)

Abstract

Agents for real applications often operate in complex, dynamic, and nondeterministic environments and thus need to function in worlds with exogenous events, other agents, and uncertain effects. In this paper, we present our reactive agent view to describe agents for real-world applications and introduce our two agent architectures: UM-PRS and Jam. UM-PRS has been applied to both physical robots and software agents and demonstrated its sufficiently powerful representation and control scheme as a general reactive agent architecture. The Jam agent architecture has evolved from UM-PRS and implemented in Java for maximum portability and mobility. We first identify agent tasks and environments and then highlight the relevant features in our agent architectures.

Keywords

Flexible Manufacture System World Model Plan Execution Agent Architecture Task Migration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Birmingham, W. P., E. H. Durfee, T. Mullen, and M. P. Wellman (1995, March). The distributed agent architecture of the university of michigan digital library (extended abstract). In AAAI Spring Symposium Series on Information Gathering from Distributed, Heterogeneous Environments.Google Scholar
  2. Durfee, E. H., M. Huber, M. Kurnow, and J. Lee (1997, February). TAIPE: Tactical assistants for interaction planning and execution. In Proceedings of the First International Conference on Autonomous Agents (Agents’ 97), Marina del Rey, California, pp. 443–450.CrossRefGoogle Scholar
  3. Fikes, R. E. and N. J. Nilsson (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208.zbMATHCrossRefGoogle Scholar
  4. Georgeff, M. P. and F. F. Ingrand (1989). Decision-making in an embedded reasoning system. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, Michigan, pp. 972–978.Google Scholar
  5. Huhns, M. N. and M. P. Singh (Eds.) (1997). Readings in Agents, Chapter 1, pp. 1–23. Morgan Kaufmann.Google Scholar
  6. Kenny, P. G., C. R. Bidlack, K. C. Kluge, J. Lee, M. J. Huber, E. H. Durfee, and T. Weymouth (1994, May). Implementation of a reactive autonomous navigation system on an outdoor mobile robot. In Association for Unmanned Vehicle Systems Annual National Symposium (AUVS-94), Detroit, MI, pp. 233–239. Association for Unmanned Vehicle Systems.Google Scholar
  7. Lee, J. (1995, March). On the design of structured circuit semantics. In AAAI Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents, pp. 127–134.Google Scholar
  8. Lee, J. (1997, January). An Explicit Semantics for Coordinated Multiagent Plan Execution. Ph. D. thesis, University of Michigan Ann Arbor, Michigan.Google Scholar
  9. Lee, J., M. J. Huber, E. H. Durfee, and P. G. Kenny (1994, March). UM-PRS: an implementation of the procedural reasoning system for multirobot applications. In Conference on Intelligent Robotics in Field, Factory, Service, and Space (CIRFFSS’ 94), Houston, Texas, pp. 842–849.Google Scholar
  10. McDermott, D. (1991, June). A reactive plan language. Technical Note YALEU/CSD/RR #864, Department of Computer Science, Yale University.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Jaeho Lee
    • 1
  1. 1.Department of Electrical EngineeringThe University of SeoulSeoulKorea

Personalised recommendations