Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
State of XCS Classifier System Research
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
What Makes a Problem Hard for XCS?
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Two Views of Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
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Using agent-based simulation experiments, we assess the relative performance of two Reinforcement Learning System (RLS) paradigms - the classical Learning Classifier System (LCS) and an enhancement, the Extended Classifier System (XCS) - in the context of playing the Iterated Prisoner's Dilemma (IPD) game. In prior research, the XCS outperforms the LCS in solving the Animats-and-Maze and Boolean Multiplexer test problems. Our work has overlaps with and is an extension of such efforts in that it allows assessment of each system's ability to (a) cope with delayed environmental feedback, (b) evolve irrational choice as the optimal behavior, and (c) cope with unpredictable input from the environment. We find that while the XCS is considerably superior to the LCS, in terms of four key performance metrics, in playing IPD games against a deterministic, reactive game-playing agent (Tit-for-Tat), the LCS does better against an unpredictable opponent (Rand) albeit with significant evolutionary effort. Further, upon examining each XCS enhancement in isolation, we see that specific LCS variants equipped with a single XCS feature, do better than the traditional LCS model and/or the XCS model in terms of particular metrics against both types of opponents but, again, usually with greater evolutionary effort. This suggests that if offline, rather than online, performance and specific performance goals are the focus, then one may construct relatively-simpler LCS variants rather than full-fledged XCS systems. Further assessments using LCS variants equipped with combinations of XCS features should help better comprehend the synergistic impacts of these features on performance in the IPD.