Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
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
The emergence of coupled sequences of classifiers
Proceedings of the third international conference on Genetic algorithms
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Adding temporary memory to ZCS
Adaptive Behavior
Robot Shaping: An Experiment in Behavior Engineering
Robot Shaping: An Experiment in Behavior Engineering
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
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Reward allotment in an event-driven hybrid learning classifier system for online soccer games
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Empirical analysis of generalization and learning in XCS with gradient descent
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Feedback of Delayed Rewards in XCS for Environments with Aliasing States
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Learning classifier system with average reward reinforcement learning
Knowledge-Based Systems
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The development of the XCS Learning Classifier System [26] has produced a stable implementation, able to consistently identify the accurate and optimally general population of classifiers mapping a given reward landscape [15,16,29]. XCS is particularly powerful within direct-reward environments, and notably within problems suitable for commercial application [3]. The application of XCS within delayed reward environments has also shown promise, although early investigations were within enviroments with a comparatively short delay to reward (e.g. [28, 19]). Subsequent systematic investigation [19,20,1,2] have suggested that XCS has difficulty accurately mapping and exploiting even simple environments with moderate reward delays. This paper summarises these results and presents new results that identify some limits and their implications. A modification to the error computation within XCS is introduced that allows the minimum error parameter to be applied relative to the magnitude of the payoff to each classifier. First results demonstrate that this modification enables XCS to successfully map longer delayed-reward enviroments.