Proceedings

Deriving Multi-Agent Coordination through Filtering Strategies

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Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Distributed AI. Pages 679-686.

Abstract

We examine an approach to multi-agent coordination that builds on earlier work on enabling single agents to control their reasoning in dynamic environments. Specifically, we study a generalization of the filtering strategy. Where single-agent filtering means tending to bypass options that are incompatible with an agent's own goals, multi-agent filtering means tending to bypass options that are incompatible with other agents' known or presumed goals. We examine several versions of multi-agent filtering, which range from purely implicit to minimally explicit, and discuss the trade -offs among these. We also describe a series of experiments that demonstrate initial results about the feasibility of using multi-agent filtering to achieve coordination without explicit negotiation.

Eithan Ephrati, Martha E. Pollack, and Sigalit Ur. 1995. Deriving Multi-Agent Coordination through Filtering Strategies. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95. IJCAI, 679–686. https://ijcai.org/proceedings/1995/89
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