Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Using emergent modularity to develop control systems for mobile robots
Adaptive Behavior - Special issue on environment structure and behavior
Evolving action selection and selective attention without actions, attention, or selection
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
An effective robotic model of action selection
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Coevolution of Robot Behavior and Central Action Selection
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
The use of evolution in a central action selection model
Applied Bionics and Biomechanics
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The development of an effective central model of action selection has already been reviewed in previous work. The central model has been set to resolve a foraging task with the use of heterogeneous behavioral modules. In contrast to collecting/depositing modules that have been hand-coded, modules related to exploring follow an evolutionary approach. However, in this paper we focus on the use of genetic algorithms for evolving the weights related to calculating the urgency for a behavior to be selected. Therefore, we aim to reduce the number of decisions made by a human designer when developing the neural substratum of a central selection mechanism.