Schema Selection and Stochastic Inference in Modular Environments

Paul Smolensky

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.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.