Evolution, Development and Learning with Predictor Neural Networks
Konstantin Lakhman, Mikhail Burtsev · 2014
Study of mechanisms, that can make possible effective learning of artificial systems in complex environments, is one of the key issues in the adaptive systems research.In this paper we make an attempt to implement and test a number of ideas motivated by brain theory.Proposed model integrates evolutionary, developmental and learning phases.The main concept of this paper is the notion of predictor neural network which provide distributed evaluation of the effectiveness of goal-directed behavior on the neuronal level.We also propose learning mechanism based on gradually inclusion of new neuronal functional groups in case when the existing behavior fails to deliver adaptive result.We performed basic computational study of the model to investigate some of its' core properties such as evolution of innate and learned behavior and dynamics of the learning process.