Attention, intention, and the structure of discourse

Running title: Structure of discourse.Includes bibliographical references (leaves 87-93)Support ... by the Advanced Research Projects Agency of the Department of Defense ... monitored by ONR under contract no. N00014-85-C-0079"Spons agency ... National Inst. of Education. Funding also provided by the System Development Foundation. Contract 400-81-0030"--Doc. resume

IMI2S: A Lightweight Framework for Distributed Computing

International audienc

Where do they look? Gaze behaviors of multiple users interacting with an embodied conversational agent

Abstract. In this paper, we describe an experiment we conducted to determine the user’s level of engagement in a multi-party scenario consisting of human and synthetic interlocutors. In particular, we were interested in the question of whether humans accept a synthetic agent as a genuine conversational partner that is worthy of being attended to in the same way as the human interlocutors. We concentrated on gaze behaviors as one of the most important predictors of conversational attention. Surprisingly, humans paid more attention to an agent that talked to them than to a human conversational partner. No such effect was observed in the reciprocal case, namely when humans addressed an agent as opposed to a human interlocutor.

Recognizing the Visual Focus of Attention for Human Robot Interaction

Sheikhi S, Odobez J-M. Recognizing the Visual Focus of Attention for Human Robot Interaction. In: Salah AA, Ruiz-del-Solar J, Meriçli Ç, Oudeyer P-Y, eds. Human Behavior Understanding. Lecture Notes in Computer Science. Vol 7559. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 99-112.We address the recognition of people’s visual focus of attention (VFOA), the discrete version of gaze that indicates who is looking at whom or what. As a good indicator of addressee-hood (who speaks to whom, and in particular is a person speaking to the robot) and of people’s interest, VFOA is an important cue for supporting dialog modelling in Human-Robot interactions involving multiple persons. In absence of high definition images, we rely on people’s head pose to recognize the VFOA. Rather than assuming a fixed mapping between head pose directions and gaze target directions, we investigate models that perform a dynamic (temporal) mapping implicitly accounting for varying body/shoulder orientations of a person over time, as well as unsupervised adaptation. Evaluated on a public dataset and on data recorded with the humanoid robot Nao, the method exhibit better adaptivity and versatility producing equal or better performance than a state-of-the-art approach, while the proposed unsupervised adaptation does not improve results