ASOCS, adaptive self-organizing concurrent system, adaptive network, adaptive algorithm
This paper presents an adaptive self-organizing concurrent system (ASOCS) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel hardware circuit. In learning mode, rules expressed as Boolean conjunctions are incrementally presented to the ASOCS. All ASOCS learning algorithms incorporate a new rule in a distributed fashion in a short, bounded time.
Original Publication Citation
Martinez, T. R. and Campbell, D. M., "A Self-Organizing Binary Decision Tree for Incrementally Defined Rule Based Systems", IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, No. 5, pp. 1231-1238, 1991.
BYU ScholarsArchive Citation
Campbell, Douglas M. and Martinez, Tony R., "A Self-Organizing Binary Decision Tree for Incrementally Defined Rule-Based Systems" (1991). All Faculty Publications. 1185.
Physical and Mathematical Sciences
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