Donald Bren School of Information and Computer Sciences

The problem of deciding which of many possible features in a training sequence are the salient or predictive ones is a well-known problem in machine learning. This problem of salience assignment is difficult when attempting to learn in an unpredictable and reactive environment. Components of Experiential Learning (CEL) is a framework for the development of computational theories of learning in this type of environment [Granger 1983, Granger and McNulty 1984). In this paper, we review the CEL processes and explain a specific computer model LURN (Learning by Unconscious ReasoNing) which illustrates the use of an incremental method for performing salience assignment. An additional con-straint on salience assignment arises from experimental results in animal psychology which indicate that living learning engines (i.e., humans and animals) make a sharp distinction between simple pairing (or strengthening) of associated events versus contingent salience assignment [Rescorla 1966). LURN calculates the contingent predictiveness of features in a noisy environment, in accordance with these experimental results.