Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Technical Note: \cal Q-Learning
Machine Learning
Adding temporary memory to ZCS
Adaptive Behavior
The Architecture of Cognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Evolutionary Computation
An Introduction to Anticipatory Classifier Systems
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 Is Thought?
Evolution of Cooperative Problem Solving in an Artificial Economy
Neural Computation
Stochastic Reinforcement in Evolutionary Multi-Agent Game Playing of Dots-and-Boxes
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Collective intelligence in combinatorial games
ASM '07 The 16th IASTED International Conference on Applied Simulation and Modelling
An abstract deep network for image classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Learning Classifier Systems are reinforcement-based learning systems that allow the development of generalised rule sets. They allow a balance between higher level generalisable learning and reinforcement, and have been used in a number of systems to introduce principles from psychology to guide methods of learning. A classifier system based on Activation Reinforcement (ARCS) is described, based on accessibility of traces in semantic memory, that provides a strength related learning technique allowing balance of generalisation and specialisation of rules. The system is based on a minimal number of design principles, is arguably simpler in design than existing classifier systems, and has clearer connections with human cognitive models. Performance on the standard Woods environments shows fast, stable learning allowing near-optimal behaviour. The methods used by ARCS have a number of differences with existing ZCS and XCS approaches, that have potential advantages regarding generalisation techniques, and for addressing sparsely rewarded domains