Schema Selection and Stochastic Inference in Modular Environments

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 3

Learning

Given a set of stimuli presenting views of some environment, how can one characterize the natural modules or "objects" that compose the environment? Should a given set of items be encoded as a collection of instances or as a set of rules? Res-tricted formulations of these questions are addressed by analysis within a new mathematical framework that describes stochastic parallel computation. An algorithm is given for simulating this computation once schemas encoding the modules of the environ-ment have been selected. The concept of computational tempera-ture is introduced. As this temperature is lowered, the system appears to display a dramatic tendency to interpret input, even if the evidence for any particular interpretation is very weak.