Intelligence without representation
Artificial Intelligence
Using emergent modularity to develop control systems for mobile robots
Adaptive Behavior - Special issue on environment structure and behavior
Learning hierarchical control structures for multiple tasks and changing environments
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Emergence of functional modularity in robots
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Layered control architectures in robots and vertebrates
Adaptive Behavior
An Behavior-based Robotics
Evolution of a Control Architecture for a Mobile Robot
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Evolvable Hardware in Evolutionary Robotics
Autonomous Robots
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolution of a subsumption architecture neurocontroller
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - AILS '04
Development of the binocular-vision-enhanced mobile robot navigation
International Journal of Intelligent Systems Technologies and Applications
Adaptive navigation for autonomous robots
Robotics and Autonomous Systems
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This paper deals with the study of scaling up behaviors in evolutive robotics (ER). Complex behaviors were obtained from simple ones. Each behavior is supported by an artificial neural network (ANN)-based controller or neurocontroller. Hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of Layered Evolution (LE), is developed and verified experimentally through computer simulations and tests in a Khepera^^^(R) micro-robot. Several behavioral modules are initially evolved using specialized neurocontrollers based on different ANN paradigms. The results show that simple behaviors coordination through LE is a feasible strategy that gives rise to emergent complex behaviors. These complex behaviors can then solve real-world problems efficiently. From a pure evolutionary perspective, however, the methodology presented is too much dependent on user's prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. Mobile robot's navigation in an unknown environment is used as a test bed for the proposed scaling strategies.