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Computational model for interpersonal attitude expression

This paper presents a plan towards a computational model of interpersonal attitudes and its integration in an embodied conversational agent (ECA). The goal is to endow an ECA with the capacity to express different interpersonal attitudes depending on the interaction context. Interpersonal attitudes can be represented by sequences of non-verbal behaviors. In our work, we rely on temporal sequence mining algorithms to extract, from a multimodal corpus, a set of temporal patterns representing interpersonal attitudes. Specifically, we propose a new temporal sequence mining algorithm called HCApriori and we evaluate it against four state-of-the-art algorithms. Results show a significant improvement of HCApriori over the other algorithms in terms of both pattern extraction accuracy and running time. The next step is to implement the temporal patterns extracted with HCApriori on an ECA.