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International Conference on Computational Collective Intelligence

ICCCI 2017: Computational Collective Intelligence pp 32–42Cite as

Adaptive Motivation System Under Modular Reinforcement Learning for Agent Decision-Making Modeling of Biological Regulation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10449))

Abstract

In this paper, an adaptive motivation system under modular reinforcement learning is suggested for agent decision-making modeling of biological regulation. For this purpose, first, main concepts of drives, rewards, action selection under modular reinforcement learning as well as an adaptive priority process are developed. Second, experiments and results are presented and analyzed demonstrating the efficiency of the suggested concepts. Finally, a discussion is given in conclusion with regard to related works. The obtained results demonstrate how the suggested adaptive motivation system can be used by an agent learning (on-line) to select appropriate actions, during a navigation task from a starting position to a goal position (external goal), i.e., in each moving step, in order to reach an external goal as well as to satisfy internal goals (drives such as hunger, thirst, …); predicting a promising result in future to demonstrate how the nature of the interaction (stimulation-drive, social-drive, …) influences the agent behavior.

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Author information

Authors and Affiliations

  1. Images, Signals, and Intelligent Systems Laboratory (LISSI/EA 3956), Paris-East University (UPEC), Senart Institute of Technology, Avenue Pierre Point, 77127, Lieusaint, France

    Amine Chohra & Kurosh Madani

Authors
  1. Amine Chohra
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  2. Kurosh Madani
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Corresponding author

Correspondence to Amine Chohra .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Chohra, A., Madani, K. (2017). Adaptive Motivation System Under Modular Reinforcement Learning for Agent Decision-Making Modeling of Biological Regulation. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-67077-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67076-8

  • Online ISBN: 978-3-319-67077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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