Classifier systems that learn internal world models

摘要

Most classifier systems learn a collection of stimulus-response rules, each of which directly acts on the problem-solving environment and accrues strength proportional to the overt reward expected from the behavioral sequences in which the rule participates. Gofer is an example of a classifier system that builds an internal model of its environment, using rules to represent objects, goals, and relationships. The model is used to direct behavior, and learning is triggered whenever the model proves to be an inadequate basis for generating behavior in a given situation. This means that overt external rewards are not necessarily the only or the most useful source of feedback for inductive change. Gofer is tested in a simple two-dimensional world where it learns to locate food and avoid noxious stimulation.