状況と行動の因果関係に着目した人間の行動モデル化手法 : —多次元時系列信号で表現された状況と離散事象で表現された行動の場合—

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Various kinds of sophisticated systems are utilized in our daily life, therefore these systems must have the ability to work considering behaviors of humans around them. In order to realize such a system, a kind of human behavior modeling method is required. In this paper, we propose a new modeling method of human behaviors based on the causality between the situation around a person and the human behavior. In this method, we suppose that a person changes his behavior according to the change of the situation around him. In this paper, a human behavior is discribed by a discrete event and the situation around the person is described by multidimensional time series signal. The causality between the time series data of the situation and the discrete event of the human behavior is defined as behavior rule. In this rule, the multidimensional time series data is modeled by HMM(Hidden Markov Model) in order to consider its temporal and spatial redundancy. Various kinds of human behavior rules are acquired based on the combination between the situation around a person and his behavior. Therefore, a set of these human behavior rules is called as a human behavior model in this paper. In the experiment, the controller operation of a radio controlled vehicle is modeled by the proposed method. The usefulness of the proposed method is examined through some experimental results with the estimation system of the next human behavior based on the generated behavior model.

状況と行動の因果関係に着目した人間の行動モデル化手法 : —多次元時系列信号で表現された状況と離散事象で表現された行動の場合—

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