Communicating neural network knowledge between agents in a simulated aerial reconnaissance system
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1999-10-03
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Stephen Quirolgico and Kip Canfield, Timothy Finin, James A. Smith, Communicating neural network knowledge between agents in a simulated aerial reconnaissance system, Proceedings. First and Third International Symposium on Agent Systems Applications, and Mobile Agents , 1999, DOI: 10.1109/ASAMA.1999.805408
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© 1999 IEEE
© 1999 IEEE
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.