Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Bucket brigade performance: I. Long sequences of classifiers
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Learning and bucket brigade dynamics in classifier systems
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
An Incremental Multiplexer Problem and Its Uses in Classifier System Research
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Limits in long path learning with XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investigations in delayed-reward (i.e. multi-step) environments have shown promise, XCS still struggles to efficiently find optimal solutions in environments with long action-chains. This paper highlights the strong relation of XCS to reinforcement learning and identifies some of the major differences. This makes it possible to add Eligibility Traces to XCS, a method taken from reinforcement learning to update the prediction of the whole action-chain on each step, which should cause prediction update to be faster and more accurate. However, it is shown that the discrete nature of the condition representation of a classifier and the operation of the genetic algorithm cause traces to propagate back incorrect prediction values and in some cases results in a decrease of system performance. As a result further investigation of the existing approach to generalisation is proposed.