Implementation of a Decision Making Algorithm Based on Somatic Markers on the Nao Robot

Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Decision making is an essential part of Autonomous Mobile Systems. Research shows that emotion is an important factor in human decision making. Therefore an increasing number of approaches using modelled emotions for decision making are developed for artificial intelligent systems. Often those approaches are only evaluated in simulated environments in which dummies are used to represent actions. However, the realisation of a real robot application also requires the handling of problems which may not occur in a simulated environment, such as long execution times. Furthermore, the adaption of existing approaches to variant applications often includes several time-consuming adjustments to the system. In this paper the implementation of an emotional decision making algorithm for the Nao robot is presented. The implementation design is based on the human brain structure and models different brain parts which are included in the decision making process. Beside the fact that the chosen structure is closer to the human model, the modular architecture allows an easy implementation of enhancements or different approaches. A key point is the easy adaption of the approach to different applications, suitable even for users without technical expertise or programming skills. As an example, a possible real life scenario is used, in which the robot is embedded in a social environment.

Keywords

Audio Data Human Decision Making Process Real Life Scenario Somatic Marker Human Voice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Damasio, A.: Descartes Error: Emotion, Reason, and the Human Brain. Putnam Press, New York (1994)Google Scholar
  2. Hoefinghoff. J., Pauli, J.: Decision making based on somatic markers. In: Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, pp. 163–168. AAAI Press, 2012Google Scholar
  3. Hoogendoorn, M., Merk, R., Roessingh, J., Treur, J.: Modelling a fighter pilot’s intuition in decision making on the basis of damasio’s somatic marker hypothesis. In: Proceedings of the 17th Congress of the International Ergonomics Association, CD-Rom (2009)Google Scholar
  4. Pimentel, C., Cravo, M.: Don’t think too much!-Artificial somatic markers for action selection. In: International Conference on Affective Computing and Intelligent Interaction, vol. 1, pp. 55–62, IEEE, Amsterdam, 2009Google Scholar
  5. Singh, O., Singla, N., Sharma, M.D.: Optimization of feedforward neural network for audio classificationsystems.Google Scholar
  6. Velásquez, J.D.: When robots weep: emotional memories and decision-making. In: Proceedings of the Fifteenth National/tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, AAAI ’98/IAAI ’98, pp. 70–75, American Association for Artificial Intelligence, Menlo Park, CA, USA, 1998Google Scholar
  7. Vitay, J., Hamker, F.: A neuroscientific view on the role of emotions in behaving cognitive agents. KI-Künstliche Intelligenz 25, 235–244 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Intelligente SystemeUniversität Duisburg-Essen, Fakultät für IngenieurwissenschaftenDuisburgGermany

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