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Understanding intentions is a complex social-cognitive task for humans, let alone machines. In this paper we discuss how the developing field of Social Signal Processing, and assessing social cues to interpret social signals, may help to develop a foundation for robotic social intelligence. We describe a taxonomy to further R&D in HRI and facilitate natural interactions between humans and robots. This is based upon an interdisciplinary framework developed to integrate: (1) the sensors used for detecting social cues, (2) the parameters for differentiating and classifying differing levels of those cues, and (3) how sets of social cues indicate specific social signals. This is necessarily an iterative process, as technologies improve and social science researchers better understand the complex interactions of vast quantities of social cue combinations. As such, the goal of this paper is to advance a taxonomy of this nature to further stimulate interdisciplinary collaboration in the development of advanced social intelligence that mutually informs areas of robotic perception and intelligence.

Most intelligence analysts currently use Information Products (IP) from multiple sources with very different characteristics to perform a variety of intelligence tasks. In order to maximize the analysts’ efficacy (and ultimately provide intelligent automation), it is important to understand how and what each IP within the set of IPs contributes to the accuracy and validity of the analytic result. This paper describes initial research toward the development of a scale, analogous to the National Imagery Interpretability Scale (NIIRS), which will measure the knowledge contribution of each of the multi-source IPs, as well as measuring the extent to which the IP set as a whole meets the enduser’s intelligence need – which is actionable knowledge. This scale, the Knowledge-NIIRS (KnIIRS), when completed, will support the measurement of the quality and quantity of information gained through multi-source IP fusion and enables the development of smart (automated) tools for analysts using the next generation of PED workstations. The results of this initial study indicate that analysts are capable of making judgments that reflect the “value” of fused information, and that the judgments they make vary along at least two dimensions. Furthermore, there are substantial and significant differences among analysts in how they make these judgments that must be considered for further scale development. We suggest that the KnIIRS objectives and its derived understandings offer important and critical insights to enable automation that will achieve the goal to deliver actionable knowledge.

Current ground robots are largely employed via tele-operation and provide their operators with useful tools to extend reach, improve sensing, and avoid dangers. To move from robots that are useful as tools to truly synergistic human-robot teaming, however, will require not only greater technical capabilities among robots, but also a better understanding of the ways in which the principles of teamwork can be applied from exclusively human teams to mixed teams of humans and robots. In this respect, a core characteristic that enables successful human teams to coordinate shared tasks is their ability to create, maintain, and act on a shared understanding of the world and the roles of the team and its members in it. The team performance literature clearly points towards two important cornerstones for shared understanding of team members: mental models and situation awareness. These constructs have been investigated as products of teams as well; amongst teams, they are shared mental models and shared situation awareness. Consequently, we are studying how these two constructs can be measured and instantiated in human-robot teams. In this paper, we report results from three related efforts that are investigating process and performance outcomes for human robot teams. Our investigations include: (a) how human mental models of tasks and teams change whether a teammate is human, a service animal, or an advanced automated system; (b) how computer modeling can lead to mental models being instantiated and used in robots; (c) how we can simulate the interactions between human and future robotic teammates on the basis of changes in shared mental models and situation assessment.