Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.