A delayed-action classifier system for learning in temporal environments

Abstract

This paper describes a modified version of the traditional classifier system called the Delayed Action Classifier System (DACS) which has been conceived for learning in environments that exhibit a rich temporal structure. DACS operates by delaying the action of appropriately tagged classifiers (called 'delayed-action classifiers') by a number of execution cycles which is encoded on the action part of these classifiers. This modification allows the rule discovery strategy, in many instances a genetic algorithm, to simultaneously explore the spaces of action (what to do) and time (when to do it). Results of initial experiments, which appear encouraging, of applying DACS to a prediction problem are presented, and the possible application of the delayed-action idea to learning in real-time environments is discussed.<>