Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning Behaviors for Environmental Modeling by Genetic Algorithm
Proceedings of the First European Workshop on Evolutionary Robotics
Mobile Robot Miniaturisation: A Tool for Investigation in Control Algorithms
The 3rd International Symposium on Experimental Robotics III
Central Action Selection Using Sensor Fusion
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Integration of evolution with a robot action selection model
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
An effective robotic model of action selection
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot animat has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm.