Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Investigating scaling of an abstracted LCS utilising ternary and s-expression alphabets
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets
Learning Classifier Systems
A learning classifier system for mazes with aliasing clones
Natural Computing: an international journal
Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Abstraction is a higher order cognitive ability that facilitates the production of rules that are independent of their associations. Experience from real-world data-mining has shown the need for such higher level rules. The game of Connect 4 is both multistep and complex, so standard Q-learning and Learning Classifier Systems perform poorly. The introduction of a novel Abstraction algorithm into an LCS is shown to improve performance in the evolution of playing strategies.