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Hierarchical Fuzzy Deep Leaning Networks for Predicting Human Behavior in Strategic Setups

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 764)

Abstract

The prediction of human behavior in strategic setups is important problem in many business situations. The uncertainty in human behavior complicates the problem to greater extent. It is generally assumed that players are rational in nature. But this assumption is far from actual scenarios. To address this, we propose hierarchical fuzzy deep learning network that automatically models cognitively without any expert knowledge. The architecture allows hierarchical network to generalize across different input and output dimensions by using matrix units rather than scalar units. The network’s performance is significantly better than previous models which depend on expert constructed features. The experiments are performed using datasets prepared from RPS game played over specified network and responder behavior from CT experiments. The proposed deep learning network has superior prediction performance as compared to others. The experimental results demonstrate efficiency of proposed approach.

Keywords

  • Behavior prediction
  • Strategic setups
  • Game theory
  • Deep learning
  • Fuzzy sets

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  • DOI: 10.1007/978-3-319-91189-2_12
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Correspondence to Arindam Chaudhuri .

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Chaudhuri, A., Ghosh, S.K. (2019). Hierarchical Fuzzy Deep Leaning Networks for Predicting Human Behavior in Strategic Setups. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_12

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