A parallel implementation of collective learning systems theory: Adaptive Learning Image Analysis System (ALIAS)

摘要

An alternative to preprogrammed rule-based Artificial Intelligence is a hierarchical network of cellular automata which acquire their knowledge through learning based on a series of trial-and-error interactions with an evaluating Environment, much as humans do. The input to the hierarchical network is provided by a set of sensors which perceive the external world. Based upon this perceived information and past experience (memory), the learning automata synthesize collections of trial responses. Periodically the automata estimate the effectiveness of these collections using either internal evaluations (unsupervised learning) or external evaluations from the Environment (supervised learning), modifying their memories accordingly. Known as Collective Learning Systems Theory, this paradigm has been applied to many sophisticated gaming problems, demonstrating robust learning and dynamic adaptivity.

rn

Based on a versatile architecture for massively parallel networks of processors for Collective Learning Systems, a Transputer-based parallel-processing image processing engine comprising 32 learning cells and 32 non-learning cells has been applied to a sophisticated image processing task: the scale-invariant and translation-invariant detection of anomalous features in otherwise "normal" images. In cooperation with Robert Bosch GmbH, this engine is currently being constructed and tested under the direction of the author at the Research Institute for Applied Knowledge Processing (FAW-Ulm) as Project ALIAS: Adaptive Learning Image Analysis System. Initial results indicate excellent detection, discrimination, and localization of anomalies.