Conversation system based on Boltzmann selection and Bayesian networks for a partner robot

Human interaction based on conversation and gestures is very important to realize the natural communication. This paper proposes a conversation system composed of topic selection module, conversation control module and utterance selection module. First, we apply a Bayesian network for the topic selection, and Boltzmann selection for the control of conversation. We apply term frequency inverse document frequency for representing the features of a document by a weight vector of terms used in the document. The experimental results show that the proposed method can select topics according to the perceptual information and human interaction.