Towards a Four Factor Theory of Anticipatory Learning

This paper takes an overtly anticipatory stance to the understanding of animat learning and behavior. It analyses four major animal learning theories and attempts to identify the anticipatory and predictive elements inherent to them, and to provide a new unifying approach based on the anticipatory nature of those elements based on five simple predictive “rules”. These rules encapsulate all the principal properties of the four diverse theories (the four factors) and provide a simple framework for understanding how an individual animat may appear to operate according to different principles under varying circumstances. The paper then indicates how these anticipatory principles can be used to define a more detailed set of postulates for the Dynamic Expectancy Model of animat learning and behavior, and to construct its computer implementation SRS/E. Some of the issues discussed are illustrated with an example experimental procedure using SRS/E.

• Type: Conference
• Year: 2003
• Where: ALS
• Authors: Mark Witkowski
• Keywords: ALS 2003, Animat Learning, Anticipatory Nature, Anticipatory Stance, Machine Learning