Towards a general distributed platform for learning and generalization

Different learning models employ different styles of generalization on novel inputs. The need for multiple styles of generalization to support a broad application base is discussed. The priority ASOCS (PASOCS) model (priority adaptive self-organizing concurrent system) is 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. 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 learning mode, 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