A neural cocktail-party processor
Biological Cybernetics
Intelligence as adaptive behavior: an experiment in computational neuroethology
Intelligence as adaptive behavior: an experiment in computational neuroethology
Neural network architectures: an introduction
Neural network architectures: an introduction
The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Learning to Perceive and Act by Trial and Error
Machine Learning
Explorations in evolutionary robotics
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Metaphorical Brain 2: Neural Networks and Beyond
The Metaphorical Brain 2: Neural Networks and Beyond
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The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.