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Agent Systems and Applications, International Symposium on / International Symposium on Mobile Agents (1999)
Palm Springs, California
Oct. 3, 1999 to Oct. 6, 1999
ISSN: 1530-2008
ISBN: 0-7695-0340-3
pp: 242
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 sub-symbolic 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.
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CITATION

S. Quirolgico, K. Canfield, T. Finin and J. Smith, "Communicating Neural Network Knowledge between Agents in a Simulated Aerial Reconnaissance System," Agent Systems and Applications, International Symposium on / International Symposium on Mobile Agents(ASAMA), Palm Springs, California, 1999, pp. 242.
doi:10.1109/ASAMA.1999.805408
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