Artificial emotion model for human-machine communication by using harmony theory

This paper deals with an artificial emotion model for 'Active Human Interface' that generates emotions and facial expressions from desire state by neural network application. The harmony theory, a type of Boltzmann machine, is employed in this paper, and for this network system, the authors show a method of learning six basic emotions (joy, anger, sadness, fear, disgust and surprise). They also formulate schemata connecting a desire state and facial expression consisting of three facial components (eye, eyebrow and mouth). Some simulation results show the successful emotion generation, demonstrating the effectiveness of the learning.<
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