Understanding intelligence
An Behavior-based Robotics
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Synaptic Modulation Based Artificial Neural Networks
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots
Biologically inspired robot behavior engineering
Macroevolutionary algorithms: a new optimization method on fitnesslandscapes
IEEE Transactions on Evolutionary Computation
Behavior analysis and training-a methodology for behaviorengineering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Complex behaviours through modulation in autonomous robot control
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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In this paper we consider one of the big challenges when constructing modular behavior architectures for the control of real systems, that is, how to decide which module or combination of modules takes control of the actuators in order to implement the behavior the robot must perform when confronted with a perceptual situation. The problem is addressed from the perspective of combinations of ANNs, each implementing a behavior, that interact through the modulation of their outputs. This approach is demonstrated using a three way predator-prey-food problem where the behavior of the individual should change depending on its energetic situation. The behavior architecture is incrementally evolved.