Biasing exploration in an anticipatory learning classifier system

Abstract

The chapter investigates how model and behavioral learning can be improved in an anticipatory learning classifier system by biasing exploration. First, the applied system ACS2 is explained. Next, an overview over the possibilities of applying exploration biases in an anticipatory learning classifier system and specifically ACS2 is provided. In ACS2, a recency bias termed action delay bias as well as an error bias termed knowledge array bias is implemented. The system is applied in a dynamic maze task and an hand-eye coordination task to validate the biases. The experiments exhibit that biased exploration enables ACS2 to evolve and adapt its internal environmental model faster. Also adaptive behavior is improved.