Supervised Neural Network Learning with an Environment Adapted Supervision Based on Motivation Learning Factors

  • Maciej Janowski
  • Adrian HorzykEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


This paper introduces an innovative approach for supervised learning systems in cases when we do not have initially defined training data sets, but we need to develop them gradually during training process on the basis of the motivation factors that come from the given environment. We suppose to gradually develop and update knowledge about the environment and use it for supervision of training MLP. In the beginning, the gradually gained knowledge does not have to be correct, but it allows to adapt a neural network still better and more efficiently in time. It is illustrated on the problem of acquiring the ability to return to the starting position optimally by a virtual robot from anywhere in an initially unknown and gradually explored maze. The proposed approach focuses on the attempt to reflect human cognitive abilities and motivation factors in an introduced model using artificial neural networks. This article presents a new approach in which the decision-making method arises from the supervised learning process controlled by the knowledge gained during maze exploration. This paper presents a model of maze exploration and knowledge-based adaptation of the neural network. The experimental results of the classical supervised learning approach and the proposed modified approach will be compared to demonstrate significant improvements.


Neural network Motivated learning Supervised learning Environment adapted supervision Knowledge-based learning Knowledge development Brain-inspired computations Cognitive systems 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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