Towards a Distributed, Environment-Centered Agent Framework

  • John R. Graham
  • Keith S. Decker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1757)


This paper will discuss the internal architecture for an agent framework called DECAF (Distributed Environment Centered Agent Framework). DECAF is a software toolkit for the rapid design, development, and execution of “intelligent” agents to achieve solutions in complex software systems. From a research community perspective, DECAF provides a modular platform for evaluating and disseminating results in agent architectures, including communication, planning, scheduling, execution monitoring, coordination, diagnosis, and learning. From a user/programmer perspective, DECAF distinguishes itself by removing the focus from the underlying components of agent building such as socket creation, message formatting, and agent communication. Instead, users may quickly prototype agent systems by focusing on the domain-specific parts of the problem via a graphical plan editor, reusable generic behaviors [9], and various supporting middle-agents [10]. This paper will briefly describe the key portions of the DECAF toolkit and as well as some of the internal details of the agent execution framework. While not all of the modules have yet been completely realized, DECAF has already been used for teaching purposes, allowing student teams, initially untutored in agent systems, to quickly build prototype multi-agent information gathering systems.


Incoming Message Execution Module Agent Framework Task Queue Hierarchical Task Network 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barrett, T., Coen, G., Hirsh, J., Obrst, L., Spering, J., Trainer, A.: MADEsmart: An integrated design environment. In: 1997 ASME Design for Manufacturing Symposium (1997)Google Scholar
  2. 2.
    Boloni, L.: BOND Objects - A White Paper. Technical Report CSD-TR 98-002, Purdue University,Department of Computer Science (February 1996)Google Scholar
  3. 3.
    Peter Bonasso, R., Kortenkamp, D., Whitney, T.: Using a robot control architecture to aoutomate space shuttle operation. In: Proceding of the Ninth Conference on Innovative Appications of AI (1997)Google Scholar
  4. 4.
    Brazier, F.M., Dunin-Keplicz, B.M., Jennings, N.R., Treur, J.: Desire: Modeling multi-agent systems in a compositional formal framework. International Journal of Cooperative Information Systems 6(1) (1997)Google Scholar
  5. 5.
    Busetta, P., Ronquist, R., Hodgson, A., Lucas, A.: Jack intelligent agents. Agent-Link News Letter (January 1990)Google Scholar
  6. 6.
    Busetta, P., Howden, N., Ronnquist, R., Hodgson, A.: Structuring BDI agents in functional clusters. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Chauhan, D.: JAFMAS: A java-based agent framework for multiagent systems development and implementation. Technical Report CS-91-06, ECECS Department, University of Cinncinati (1997)Google Scholar
  8. 8.
    Cohen, P.R., Levesque, H.J.: Cohen and Hector J. Levesque. Intention is choice with commitment. Artificial Intelligence 42(3), 213–261 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Decker, K.S., Pannu, A., Sycara, K., Williamson, M.: Designing behaviors for information agents. In: Proceedings of the 1st Intl. Conf. on Autonomous Agents, Marina del Rey, February 1997, pp. 404–413 (1997)Google Scholar
  10. 10.
    Decker, K.S., Sycara, K., Williamson, M.: Middle-agents for the internet. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Nagoya, Japan, August 1997, pp. 578–583 (1997)Google Scholar
  11. 11.
    Decker, K.S., Lesser, V.R.: Quantitative modeling of complex computational task environments. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, Washington, July 1993, pp. 217–224 (1993)Google Scholar
  12. 12.
    Decker, K.S., Sycara, K.: Decker and Katia Sycara. Intelligent adaptive information agents. Journal of Intelligent Information Systems 9(3), 239–260 (1997)CrossRefGoogle Scholar
  13. 13.
    Erol, K., Nau, D., Hendler, J.: Semantics for hierarchical task-network planning. Technical report CS-TR-3239, UMIACS-TR-94-31, Computer Science Dept., University of Maryland (1994)Google Scholar
  14. 14.
    James Firby, R.: Task networks for controlling continuous processes, Seattle, WA (1996)Google Scholar
  15. 15.
    Fisher, M.: Introduction to concurrent metatem (1996)Google Scholar
  16. 16.
    Garvey, A., Humphrey, M., Lesser, V.: Task interdependencies in design-to-time real-time scheduling. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, Washington, July 1993, pp. 580–585 (1993)Google Scholar
  17. 17.
    Garvey, A., Lesser, V.: Design-to-time real-time scheduling. COINS Technical Report 91–72, University of Massachusetts, (1991); To appear, IEEE Transactions on Systems, Man and Cybernetics (1993)Google Scholar
  18. 18.
    Greenwald, L., Dean, T.: A conditonal schdeuling approach to designing real-time systems. The Journal of AAAI (1998)Google Scholar
  19. 19.
    Grosz, B., Kraus, S.: Collaborative plans for group activities. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambéry, France (August 1993)Google Scholar
  20. 20.
    Hartley, S.J.: Concurrent Programming, The Java Programming Lanaguage. Oxford University Press, Drexel University (1998)Google Scholar
  21. 21.
    Hayes-Roth, F., Erman, L., Fouse, S., Lark, J., Davidson, J.: ABE: A cooperative operating system and development environment. In: Bond, A.H., Gasser, L. (eds.) Readings in Distributed Artificial Intelligence, pp. 457–490. Morgan Kaufmann, San Francisco (1988)Google Scholar
  22. 22.
    Horling, B., Lesser, V., Vincent, R., Bazzan, A., Xuan, P.: Diagnosis as an integral part of multi-agent adaptability. Tech Report CS-TR-99-03, UMass (1999)Google Scholar
  23. 23.
    Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)Google Scholar
  24. 24.
    Petrie, C.J.: Agent-based engineering, the web, and intelligence. IEEE Expert, Los Alamitos (December 1996)Google Scholar
  25. 25.
    Rao, A.S., Georgeff, M.P.: BDI agents: From theory to practice. In: Proceedings of the First International Conference on Multi-Agent Systems, San Francisco, pp. 312–319. AAAI Press, Menlo Park (1995)Google Scholar
  26. 26.
    Rosenschein, J.S., Zlotkin, G.: Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge (1994)Google Scholar
  27. 27.
    Russell, S., Wefald, E.: Do the Right Thing: Studies in Limited Rationality. MIT Press, Cambridge (1991)Google Scholar
  28. 28.
    Sandholm, T., Lesser, V.: Utility-based termination of anytime agents. CS Technical Report 94–54, Univ. of Massachusetts (1994)Google Scholar
  29. 29.
    Simmons, R.: Becoming increasingly reliable (1996)Google Scholar
  30. 30.
    Sycara, K., Decker, K.S., Pannu, A., Williamson, M., Zeng, D.: Distributed intelligent agents. IEEE Expert 11(6), 36–46 (1996)CrossRefGoogle Scholar
  31. 31.
    Wagner, T., Garvey, A., Lesser, V.: Complex goal criteria and its application in design-to-criteria scheduling. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, Providence (July 1997)Google Scholar
  32. 32.
    Wallace, S.A., Laird, J.E.: Toward a methodology for AI architecture evaluation: Comparing Soar and CLIPS. In: Jennings, N.R. (ed.) ATAL 1999. LNCS (LNAI), vol. 1757. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  33. 33.
    Williamson, M., Decker, K.S., Sycara, K.: Executing decision-theoretic plans in multi-agent environments. In: AAAI Fall Symposium on Plan Execution (November 1996) AAAI Report FS-96-01Google Scholar
  34. 34.
    Williamson, M., Decker, K.S., Sycara, K.: Unified information and control flow in hierarchical task networks. In: Proceedings of the AAAI-96 workshop on Theories of Planning, Action, and Control (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • John R. Graham
    • 1
  • Keith S. Decker
    • 1
  1. 1.Department of Computer and Information SciencesUniversity of DelawareNewarkUSA

Personalised recommendations