Classifier systems (CS) are used as control architectures for simulated animals or robots in order to decide what to do at each time. We will explain why these systems are good candidates for action selection mechanisms of Non Player Characters. After having described different classifier systems, we will introduce a new CS architecture, acting in a multi-agent environment, which is adapted to the specific constraints of the `Massively Multi-players Online Role Playing Games'.

A simple computational model of secondary immune response is used to provide a Pittsburgh style classifier system with the ability to improve its reaction to already encountered situations in a dynamical cyclic learning environment. Main results obtained with our core algorithm (YaSais) on Time Dependent Optimization problems are briefly reminded before to introduce the Pittsburgh Immune Classifier System (PICS) which is then experimentally evaluated on both a static and dynamical multiplexer problem. Eventually, the Lazy Optimality Effect, keystone of YaSais' efficiency, is re-examinated in PICS. Suggested enhancements are then experimentally evaluated. 1

There are many people I would like to thank for their support. Principally I thank my family, Sue, Nick, Katie and William, and my Supervisor Larry Bull. All have shown tolerance, forbearance, support and inspiration well beyond the call of duty. In addition Dave Wyatt and Praminda Caleb-Solly have been very knowledgeable, approachable and helpful, I owe them a lot. The UWE LCSG has been a key source of ideas, knowledge and inspiration. Books and papers cannot provide such a level of understanding and debate about fundamental issues. Amongst the key players Larry Bull, Alwyn Barry, Dave Wyatt, Matt Studley, Chris Stone, Tony Pipe, Brian Carse and Rob Smith have always provided difficult and thought provoking questions, and sometimes even answers. Lastly thanks to Paul Lewis, Joe Mackenzie both of BT, Terry Fogarty, and Roger Miles who in there own ways facilitated the move from being a competent project manager to entering the (from Huxley) “brave new world that hath such people in’t ” of evolutionary and neural computing. This thesis investigates a hybrid of evolutionary computing and neural computing which long has been a goal of machine learning. X-NCS is a neural and hence a more complex version of XCS (Wilson 1995), the pre-eminent accuracy based Learning Classifier System (LCS) (Holland, 1986). XCS differs from other