Learning to predict the effects of actions: Synergy between rules and landmarks

A developing agent must learn the structure of its world, beginning with its sensorimotor world. It learns rules to predict how its motor signals change the sensory input it receives. It learns the limits to its motion. It learns which effects of its actions are unconditional and which effects are conditional, including what they depend on. We present preliminary results evaluating an implemented computational model of this important kind of foundational developmental learning. Our model demonstrates synergy between the learning of landmarks representing important qualitative distinctions, and the learning of rules that exploit those distinctions to make reliable predictions. These qualitative distinctions make it possible to define discrete events, and then to identify predictive rules describing regularities among events and the values of context variables. The attention of the learning agent is focused by a stratified model that structures the set of variables, and the structure of the stratified model is simultaneously created by the learning process.