Using Classifier Systems as Adaptive Expert Systems for Control
Résumé
In complex simulations involving several interacting agents, the behavior of the overall program is difficult to predict and control. As a consequence, the designers have to adopt a trial-and-error strategy. In this paper we want to show that helping experts to design simulation automata as classifier systems (CSs) by hand and using a semi-automated improvement functionality can be a very efficient engineering approach. Through the example of a simple multiagent simulation, we show how simulation automata can be implemented into the CS formalism. Then we explain how the obtained CS can be improved either by hand or thanks to adaptive algorithms. We first show how giving indications on the non-Markov character of the problems faced by the classifiers can help the experts to improve the controllers and we explain why adding modularity in the CS formalism is important. Then we show how the adaptive algorithms inherent to Learning Classifier Systems (LCSs)1 can be used in such a context, we discuss our methodology and we present an experimental study of the efficiency of this approach. Finally, we point to difficulties raised by our perspective, we present directions for future research and conclude.
In order to make clear that we sometimes use the Classifier Systems formalism without applying learning algorithms, we will distinguish Classifier Systems (CS) as a formalism and Learning Classifier Systems (LCS) throughout this paper.
We call them automata whether these programs are explicitly implemented as finite state machines or not.