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Communicating neural network knowledge between agents in a simulated aerial reconnaissance system

Authors: Stephen Quirolgico, Kip Canfield

Book Title: Proceedings of the First International Symposium on Agent Systems and Applications,

Date: October 03, 1999

Abstract: In order to maintain their performance in a dynamic environment, agents may be required to modify their learning behavior during run-time. If an agent utilizes a rule-based system for learning, new rules may be easily communicated to the agent in order to modify the way in which it learns. However, if an agent utilizes a connectionist-based system for learning, the way in which the agent learns typically remains static. This is due, in part, to a lack of research in communicating subsymbolic information between agents. In this paper, we present a framework for communicating neural network knowledge between agents in order to modify an agent�s learning and pattern classification behavior. This framework is applied to a simulated aerial reconnaissance system in order to show how the communication of neural network knowledge can help maintain the performance of agents tasked with recognizing images of mobile military objects.

Type: InProceedings

Tags: kqml, agent, learning, multiagent system

Google Scholar: TQDUelNPwEgJ

Number of Google Scholar citations: 5 [show citations]

Number of downloads: 1976

 

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