Constructive higher-order network that is polynomial time
Neural Networks
Seeing the light: artificial evolution, real vision
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Fuzzy and Neural Approaches in Engineering
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Introduction to Reinforcement Learning
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Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
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Controlling Excessive Fuzziness in a Fuzzy Classifier System
Proceedings of the 5th International Conference on Genetic Algorithms
State of XCS Classifier System Research
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An Introduction to Learning Fuzzy Classifier Systems
Learning Classifier Systems, From Foundations to Applications
A Self-Adaptive Classifier System
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Zcs: A zeroth level classifier system
Evolutionary Computation
Cooperation and community structure in artificial ecosystems
Artificial Life
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
On the effects of node duplication and connection-oriented constructivism in neural XCSF
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Discrete dynamical genetic programming in XCS
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Dynamical genetic programming in xcsf
Evolutionary Computation
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For artificial entities to achieve true autonomy and display complex life-like behaviour they will need to exploit appropriate adaptable learning algorithms. In this sense adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn novel behaviours. This paper explores the potential of using constructivism within the neural classifier system architecture as an approach to realise such behaviour. The system uses a rule structure in which each is represented by an artificial neural network. Results are presented which suggest it is possible to allow appropriate internal rule complexity to emerge during learning and that the structure indicates underlying features of the task.