Human-Machine Interaction: Causal Dynamical Networks

  • M.M. Khoshyaran Economics Traffic Clinic - ETC, 34 Avenue des Champs Elyses, 75008, Paris France
Keywords: Dynamical interactions, causal dynamical network, disordering locality, non-local links, complexity, entropy, disequilibrium.


The objective of this paper is to introduce a modified version of the Causal Dynamical Networks (CDN) algorithm for application in the human-machine interaction. It is demonstrated that an individual does not interact with one robot, but with a multitude of personalities stored in the robot. These personalities are independent of each other. A robot thus does not have a unique personality. In order for a robot to become a unique individual a new algorithm is proposed. The new algorithm is called the Causal Form Fluctuation Network (CEFN). It is shown that such an algorithm can help machines develop similar to human general intelligence capabilities such as interpretation, wisdom (acquiring knowledge), and prediction (intuition). Also to be able to make decisions, have ideas, and imaginations.


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How to Cite
Khoshyaran, M. (2017). Human-Machine Interaction: Causal Dynamical Networks. Journal of Progressive Research in Mathematics, 12(1), 1789-1802. Retrieved from