Classifier systems and genetic algorithms
Artificial Intelligence
Intelligence without representation
Artificial Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Hierarchical chunking in classifier systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
An introduction to genetic algorithms
An introduction to genetic algorithms
Introduction to Robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Knowledge Growth in an Artificial Animal
Proceedings of the 1st International Conference on Genetic Algorithms
Properties of the Bucket Brigade
Proceedings of the 1st International Conference on Genetic Algorithms
Journal of Intelligent and Robotic Systems
Automatic Symbolic Modelling of Co-evolutionarily Learned Robot Skills
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
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In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.