A Rosetta stone for connectionism
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
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Artificial Life
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Reinforcement Learning
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolutionary Computation
Representational Difficulties with Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Mapping Neural Networks into Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
On Using Constructivism in Neural Classifier Systems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
TCS Learning Classifier System Controller on a Real Robot
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Cooperation and community structure in artificial ecosystems
Artificial Life
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
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adaptive mutation in XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks
Learning Classifier Systems
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Learning local linear Jacobians for flexible and adaptive robot arm control
Genetic Programming and Evolvable Machines
Evolving spiking networks with variable resistive memories
Evolutionary Computation
| Hi-index | 0.00 |
For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.