Learning classifier systems for decision making in continuous-valued domains

This thesis investigates Learning Classifier System architectures for decision making in continuous-valued domains. The information contained in continuous-valued domains is not always conveniently expressed using the ternary representation typically used by Learning Classifier Systems and an interval-based representation is a natural choice. Two intervalbased representations recently proposed are analysed, together with their associated operators. Evidence of considerable representational and operator bias is found. A new interval-based representation is proposed that is more straightforward than the previous ones and its bias is analysed. Learning Classifier Systems are compared for online environments that consist of real-valued states and which require every action made by the agent to count towards its performance. Two Learning Classifier System architectures are considered, XCS and ZCS. An interval representation is used for the rule conditions and a roulette wheel is used for action selection. The performance of these two Learning Classifier System architectures is investigated on a set of abstract environments with both deterministic and stochastic reward functions. Although XCS clearly delivers superior performance in the deterministic environments tested, the simple ZCS architecture is found to be robust and able to equal or exceed the performance of XCS in the stochastic environments tested, especially those with more demanding characteristics. Aspects of the algorithm and parameter set of ZCS are studied on problems with real-valued states and a Boolean action space. Increased performance is found to result from the use of an update algorithm based on that of NewBoole, an earlier strength-based Learning Classifier System. A new operator, specialize, is introduced and found to be effective in combatting over general classifiers. The modified algorithm and parameter set is tested on several variants of three real-valued test problems. The resulting Learning Classifier System is applied to simulated Foreign Exchange trading using an experimental setup and data previously presented in the literature. Results show that a simple Learning Classifier System is able to achieve a positive excess return in simulated trading.