generalization, adaptive self-organizing concurrent system, ASOCS
Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During leaming mode, the PASOCS incorporates rules, with attached priorities, which represent the application being learned. Learning is accomplished in a distributed fashion in time logarithmic in the number of rules. The new model has significant learning time and space complexity improvements over previous models.
Original Publication Citation
Martinez, T. R., and Hughes, B., "Towards a General Distributed Platform for Learning and Generalization", Proceedings of the Conference on Artificial Neural Networks and Expert Systems ANNES'93, pp. 216-219, 1993.
BYU ScholarsArchive Citation
Hughes, Brent W. and Martinez, Tony R., "Towards a General Distributed Platform for Learning and Generalization" (1993). Faculty Publications. 1176.
Physical and Mathematical Sciences
© 1993 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information