We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map ( ) from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trained . This method enables to omit the troublesome process to specify types of information which should be used to build the estimator. Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed. As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts. On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes. Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate. And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered. 1. Introduction Estimation of human intention is quite practical for various applications such as assistance software [1], prediction of users’ requests on the internet [2], and marketing [3]. In the robotics fields, realization of the intention estimator is desired especially for power assist systems [4] and for cooperative robots [5] since users’ intentions are significant for their control. A function to estimate users’ internal status and their intention is embedded in social robots [6] or interactive human-friendly robots [7, 8]. Thus, such technology is becoming a requisite technology to realize advanced human-computer interaction. Interaction techniques used in their applications are, however, designed on a case-by-case basis since practical design methodology for a general artificial system has not yet been established. Especially, function of existing intention estimators were
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