Abstract
Coordination is a key functionality in multi-agent systems, and mechanisms for achieving coordinated behaviors have been well-studied. One important observation has been that different mechanisms have correspondingly different performance characteristics, and that these can change dramatically in different environments (i.e., no one mechanism is best for all domains). A more recent observation is that one can describe possible mechanisms in a domain-independent way, as simple or complex responses to certain dependency relationships between the activities of different agents. Thus agent programmers can separate encoding agent domain actions from the solution to particular coordination problems that may arise. This paper explores the specification of a large range of coordination mechanisms, for the common hard “enablement” (or “happens-before”) relationship between tasks at different agents. It also explores the impact of task environment characteristics on the choice/performance of these mechanisms. Essentially, a coordination mechanism can be described as a set of protocols (possibly unique to the mechanism), and as an associated automatic re-writing of the specification of the domain-dependent task (expressed as an augmented HTN). The idea about the separation of general knowledge and domain-dependent knowledge is explained. A general method to address the relationships between application domains and agent coordination is introduced. This paper also presents a concrete implementation of this idea in the DECAF agent architecture and an initial exploration of the separation of domain action from meta-level coordination actions for eight simple coordination mechanisms.
This work is supported by NSF Grant No. 9733004.
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References
C. Castelfranchi and R. Conte. Distributed artificial intelligence and social science: Critical issues. In Foundations of Distributed Artificial Intelligence, Chapter 20, 1996.
W. Chen and K. Decker. Coordination Mechanisms for Dependency Relationships among Multiple Agents. In Proceedings of AAMAS 2002, Bologna, Italy, July, 2002.
W. Chen and K. Decker. Developing Alternative Mechanisms for Multiagent Coordination. In Intelligent Agents and Multi-Agent Systems, pages 63–76, Springer-Verlag, 2002.
C. Dellarocas and M. Klein. An experimental evaluation of domain-independent fault handling services in open multi-agent systems. In Proceedings of ICMAS’00, Boston, MA, USA, 2000.
R. Davis and R. G. Smith. Negotiation as a metaphor for distributed problem solving. Artificial Intelligence, 20(1):63–109, Jan. 1983.
K. Decker, S. Khan, C. Schmidt, and D. Michaud. Extending a multi-agent system for genomic annotation. In M. Klusch and F. Zambonelli, editors, Cooperative Information Agents IV, pages 106–117. Springer-Verlag, 2001.
K. S. Decker and V. R. Lesser. Designing a family of coordination algorithms. In Proceedings of the First International Conference on Multi-Agent Systems, pages 73–80, San Francisco, June 1995. AAAI Press. Longer version available as UMass CS-TR 94–14.
K. S. Decker and J. Li. Coordinating mutually exclusive resources using gpgp. Autonomous Agents and Multi-Agent Systems, 3, 2000.
K. S. Decker and K. Sycara. Intelligent adaptive information agents. Journal of Intelligent Information Systems, 9(3):239–260, 1997.
K. Erol, D. Nau, J. Hendler, and R. Tsuneto. A critical look at critics in htn planning. In Proceedings of the IJCAI95, Montreal, Canada, Aug. 1995.
C. B. Excelente-Toledo and N. R. Jennings. Learning to select a coordination mechanism. In Proceedings of AAMAS02, pages 1106–1113, 2002.
Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. 55(1): 119–139, 1997.
G. Weiss. Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence, pages 88–92. MIT Press, 1999.
J. Graham and K. Decker. Towards a distributed, environment-centered agent framework. In Intelligent Agents IV, Agent Theories, Architectures, and Languages. Springer-Verlag, 2000.
M. Tambe. Teamwork in real-world, dynamic environments. In Proceedings of the International conference on multi-agent systems(ICMAS96), 1997.
M. N. Prasad and V. Lesser. Learning situation-specific coordination in generalized partial global planning. In AAAI Spring Symposium on Adaptation, Co-evolution and Learning in Multiagent Systems, Stanford, Mar. 1996.
R. Schapire. A brief introduction to boosting. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
S. Sen, M. Sekaran, and J. Hale. Learning to coordinate withotu sharing information. In Proceedings of AAAI’94, Seattle, WA, USA, July 1994.
S. Rustogi and M. Singh. Be patient and tolerate imprecision: How autonomous agents can coordinate effectively. In Proceedings of IJCAI’99, Stockholm, Sweden, Aug. 1999.
S. S. Sen and N. Arora. Effect of local information on group behavior. In Proceedings of the International Conference on MAS, pages 315–321, 1996.
T. Sugawara and V. R. Lesser. On-line learning of coordination plans. Computer Science Technical Report 93-27, University of Massachusetts, 1993.
S. Willmott and B. Faltings. The benefits of environment adaptive organizations for agent coordination and network routing problems. In Proceedings of IJCAI’99, Stockholm, Sweden, Aug. 1999.
T. Hogg and B. Huberman. Controlling chaos in distributed systems. In IEEE Transactions on Systems, Man, and Cybernetics, pages 1325–1332, 1991.
T. Wagner, A. Garvey, and V. Lesser. Complex goal criteria and its application in designto-criteria scheduling. Providence, July 1997.
Y. So and E. Durfee. Designing organizations for computational agent. In K. C. In M. J. Pritula and L. Gasser, editors, Simulating Organizations, pages 47–64. AAAI press, 1998.
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Chen, W., Decker, K. (2004). Applying Coordination Mechanisms for Dependency Relationships Under Various Environments. In: Wagner, T.A. (eds) An Application Science for Multi-Agent Systems. Multiagent Systems, Artificial Societies, and Simulated Organizations, vol 10. Springer, Boston, MA. https://doi.org/10.1007/1-4020-7868-4_11
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