Extending Multi-agent Cooperation by Overhearing
Much cooperation among humans happens following a common pattern: by chance or deliberately, a person overhears a conversation between two or more parties and steps in to help, for instance by suggesting answers to questions, by volunteering to perform actions, by making observations or adding information. We describe an abstract architecture to support a similar pattern in societies of artificial agents. Our architecture involves pairs of so-called service agents (or services) engaged in some tasks, and unlimited number of suggestive agents (or suggesters). The latter have an understanding of the work behaviours of the former through a publicly available model, and are able to observe the messages they exchange. Depending on their own objectives, the understanding they have available, and the observed communication, the suggesters try to cooperate with the services, by initiating assisting actions, and by sending suggestions to the services. These in effect may induce a change in services behaviour. Our architecture has been applied in a few industrial and research projects; a simple demonstrator, implemented by means of a BDI toolkit, JACK Intelligent Agents, is discussed in detail.
KeywordsAgent technologies systems and architectures Web information systems and services
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- 2.M. Benerecetti, F. Giunchiglia, and L. Serafini. Model Checking Multiagent Systems. Journal of Logic and Computation, 8(3), 1998. 49Google Scholar
- 3.E. Blanzieri and P. Giorgini. From Collaborating Filtering to Implicit Culture: a General Agent-Based Framework. In Proc. of the Workshop on Agent-Based Recommender Systems (WARS), Barcelona, Spain, June 2000. ACMPress. 41, 49Google Scholar
- 4.P. Busetta and R. Kotagiri. An architecture for mobile BDI agents. In Proc. of the 1998 ACM Symposium on Applied Computing (SAC’98), Atlanta, Georgia, USA, 1998. ACMPress. 49Google Scholar
- 5.A. Cimatti, M. Roveri, and P. Traverso. Automatic OBDD-based Generation of Universal Plans in Non-Deterministic Domains. In Proc. of the 15th Nat. Conf. on Artificial Intelligence (AAAI-98), Madison, Wisconsin, 1998. AAAI-Press. 49Google Scholar
- 6.J. Doran, S. Franklin, N. Jennings, and T. Norman. On Cooperation in Multi-Agent Systems. The Knowledge Engineering Review, 12(3), 1997. 40Google Scholar
- 7.A. F. Dragoni, P. Giorgini, and L. Serafini. Updating Mental States from Communication. In Intelligent Agents VII. Agent Theories, Architectures, and Languages-7th Int. Workshop, LNAI, Boston, MA, USA, July 2000. Springer-Verlag. 42Google Scholar
- 8.M. Klusch, editor. Intelligent Information Systems. Springer-Verlag, 1999. 40Google Scholar
- 9.T. Oates, M. Prasad, and V. Lesser. Cooperative Information Gathering: A Distributed Problem Solving Approach. IEE Proc. on Software Engineering, 144(1), 1997. 40Google Scholar
- 10.A. S. Rao. Means-End Plan Recognition: Towards a Theory of Reactive Recognition. In Proc. of the 4th Int. Conf. on Principles of Knowledge Representation and Reasoning (KRR-94), Bonn, Germany, 1994. 42Google Scholar
- 11.A. S. Rao and M. P. Georgeff. An Abstract Architecture for Rational Agents. In Proc. of the 3rd Int. Conf. on Principles of Knowledge Representation and Reasoning (KR’92), San Mateo, CA, 1992. Morgan Kaufmann Publishers. 49Google Scholar
- 12.S. Zilberstein. Using Anytime Algorithms in Intelligent Systems. AI Magazine, 17(3), Fall 1996. 49Google Scholar