ASOCS: Towards Bridging Neural Network and Artificial Intelligence Learning
نویسنده
چکیده
A new class of connectionist architectures is presented called ASOCS (Adaptive Self-Organizing Concurrent Systems) [3,4]. ASOCS models support efficient computation through self-organized learning and parallel execution. Learning is done through the incremental presentation of rules and/or examples. Data types include Boolean and multi-state variables; recent models support analog variables. The model incorporates rules into an adaptive logic network in a parallel and self organizing fashion. The system itself resolves inconsistencies and generalizes as the rules are presented. After an introduction to the ASOCS paradigm, the abstract introduces current research thrusts which significantly increase the power and applicability of ASOCS models. For simplicity, we discuss only boolean mappings in the ASOCS overview.
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