Alternate Adaptive Agent Architectures and Behavioral Consequences
David A. Gaines, Ramakrishnan Pakath · Journal of the Association for Information Systems · 2004
The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for adaptive agent construction.Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve "optimal" classifier sets in particular applications requiring rational thought.We examine LCS/XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner's Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays.We systematically perturb a "conventional" IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant with the XCS agent in terms of a number of performance measures.Our intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.