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Proceedings Paper

Learning comunication strategies for distributed artificial intelligence
Author(s): Michael Kinney; Costas Tsatsoulis
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Paper Abstract

We present a methodology that allows collections of intelligent system to automatically learn communication strategies, so that they can exchange information and coordinate their problem solving activity. In our methodology communication between agents is determined by the agents themselves, which consider the progress of their individual problem solving activities compared to the communication needs of their surrounding agents. Through learning, communication lines between agents might be established or disconnected, communication frequencies modified, and the system can also react to dynamic changes in the environment that might force agents to cease to exist or to be added. We have established dynamic, quantitative measures of the usefulness of a fact, the cost of a fact, the work load of an agent, and the selfishness of an agent (a measure indicating an agent's preference between transmitting information versus performing individual problem solving), and use these values to adapt the communication between intelligent agents. In this paper we present the theoretical foundations of our work together with experimental results and performance statistics of networks of agents involved in cooperative problem solving activities.

Paper Details

Date Published: 20 August 1992
PDF: 12 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139954
Show Author Affiliations
Michael Kinney, Univ. of Kansas (United States)
Costas Tsatsoulis, Univ. of Kansas (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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