USING ACCURACY-BASED LEARNING CLASSIFIER SYSTEMS FOR ADAPTABLE STRATEGY GENERATION IN GAMES AND INTERACTIVE VIRTUAL SIMULATIONS

Abstract

Historically, the artificial intelligence (AI) of interactive virtual simulations or games is usually driven by pre-defined static scripts. One of the disadvantages of such scripted opponents is that they can be deciphered and countered by an intelligent user. Thus, the user has the opportunity to find weaknesses and an easy solution against the virtual simulation, which diminishes the efficiency aspect of a training session or entertaining value drastically. While randomization can be used to mask the static behaviour of a scripted AI, it is possible to develop much richer solutions by applying Learning Classifier System (LCS) techniques to create agents with intelligent-like behaviors. Learning Classifier Systems are rule-based machine learning techniques that rely on a Genetic Algorithm to discover a knowledge map used to classify an input space into a set of actions.In this paper, we propose the use of an unsupervised machine learning technique called Accuracy-based Learning Classifier Systems (XCS) for adaptable strategy generation that can be used in virtual simulations or games. XCS use a Genetic Algorithm to evolve a knowledge base in the form of rules. The performance and adaptability of the strategies and tactics developed with the XCS is analyzed by facing these against scripted opponents on a real time strategy game. According to our experiments, the rulesets are able to adapt to a wide array of behaviors from its opponents, as opposed to a linear game script, which is limited in its ability to adapt to its environment.