{"title":"A computational model of artificial emotion by using harmony theory and genetic algorithm","authors":"F. Hara, S. Mogi","doi":"10.1109/ROMAN.1993.367682","DOIUrl":null,"url":null,"abstract":"This paper deals with a computational model of artificial emotion for \"Active Human Interface\" that generates emotion and facial expressions from the emotional evaluation state of external stimuli given to the model using the harmony theory, neural network and genetic algorithm. The harmony theory, a type of Boltzmann machine, is employed in this paper, and for this network system, we show a method of learning six basic emotions (joy, anger, sadness, fear, disgust and surprise). We also formulate schemata connecting emotional evaluation states and facial expressions consisting three facial components (eye, eyebrow and mouth). Simulation results show the successful emotion generation demonstrating the effectiveness of the genetic algorithm learning.<<ETX>>","PeriodicalId":270591,"journal":{"name":"Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1993-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1993 2nd IEEE International Workshop on Robot and Human Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROMAN.1993.367682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper deals with a computational model of artificial emotion for "Active Human Interface" that generates emotion and facial expressions from the emotional evaluation state of external stimuli given to the model using the harmony theory, neural network and genetic algorithm. The harmony theory, a type of Boltzmann machine, is employed in this paper, and for this network system, we show a method of learning six basic emotions (joy, anger, sadness, fear, disgust and surprise). We also formulate schemata connecting emotional evaluation states and facial expressions consisting three facial components (eye, eyebrow and mouth). Simulation results show the successful emotion generation demonstrating the effectiveness of the genetic algorithm learning.<>
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