A topological neural map for on-line learning: emergence of obstacle avoidance in a mobile robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
ALIFE Proceedings of the sixth international conference on Artificial life
Emergence of functional modularity in robots
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
An artificial life model for investigating the evolution of modularity
Proceedings from the international conference on complex systems on Unifying themes in complex systems
Evolving Artificial Neural Networks that Develop in Time
Proceedings of the Third European Conference on Advances in Artificial Life
Topology Design of Feedforward Neural Networks by Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
Evolution of communication and language using signals, symbols, andwords
IEEE Transactions on Evolutionary Computation
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A convincing argument has been made in the literature for the advantages gained by using modular neural networks (NN) instead of homogeneous structures. Here, a modular NN design was used in conjunction with evolutionary algorithm methods to evolve an animat capable of learning behavioural patterns at several levels of complexity. A parallel was drawn between the training of the animat and the stages of learning experienced by the young of many animals. Following movement learning, the animat was eventually capable of navigating an environment and avoiding obstacles. Discussion is made of how such an animat with many degrees of freedom can develop complex behavioural patterns.