Communicating neural network knowledge between agents in a simulated aerial reconnaissance system
Links to Fileshttps://ieeexplore.ieee.org/document/805408
MetadataShow full item record
Type of Work13 pages
conference papers and proceedings preprints
Citation of Original PublicationStephen 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
RightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
© 1999 IEEE
UMBC Ebiquity Research Group
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.