Developmental Neural Networks for Agents
A system for generating neural networks to control simulated agents is described. The networks develop during the lifetime of the agents in a process guided by the genotype and affected by the agent’s experience. Evolution was used to generate effective controllers of this kind for orientation and discrimination tasks as introduced by Beer. This scheme allows these behaviours to be generated quickly and effectively and may offer insights into the effects of developmental processes on cognition. For example, development may allow environmental regularities to be recognised without genetic prespecification. Possible future research into the abilities of these controllers to adapt to radical changes and to undertake widely varying tasks with a single genotype is described.
KeywordsNeural Network Horizontal Position Chemical Gradient Modular Neural Network Developmental Controller
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