Integrating reactive, sequential, and learning behavior using dynamical neural networks
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolutionary Design by Computers with CDrom
Evolutionary Design by Computers with CDrom
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Machine Learning
Proceedings of the First European Workshop on Evolutionary Robotics
Evolving Objects: A General Purpose Evolutionary Computation Library
Selected Papers from the 5th European Conference on Artificial Evolution
Evolving integrated controllers for autonomous learning robots using dynamic neural networks
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The dynamics of action selection
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Evolution of Voronoi based fuzzy recurrent controllers
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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The idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and the bottom-up designer-free approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and showing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail.