Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.

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Abstract-The paper describes a design specification process for the development of novel and intelligent surgical robots. Nowadays, surgical robots are usually controlled by the surgeons manually by using teleoperation. The possibility to carry out simple surgical actions automatically has been the subject of academical research, but very few real-world applications exist. The main objective of this research is to address realistic case studies and develop systems and methods to provide surgeons with autonomous robotic assistants, performing basic surgical actions by combining sensing, dexterity and cognitive capabilities. This goal can only be achieved by means of a formal and rigorous assesment of surgical requirements, so that they can be analysed and translated into behavioral specifications for an autonomous robotic system. Therefore, the paper describes the application of Requirements Engineering to surgical knowledge formalization and propose a methodology for the transformation of requirements into formal models of robotic tasks.

This paper presents the results from an experiment with a conversational human-robot interaction system aimed at long-term support for diabetic children. The system offers a set of activities aimed to help a child to improve its capability to manage diabetes. There is a large body of literature on the techniques that artificial agents can use to establish and maintain long-term social-emotional relationships with their users. The novel aspect in the present study is the inclusion of off-activity talk interspersed within talk pertaining the activity at hand and aimed to elicit the child's self-disclosure. The children in our study (N=20, age 11-14) were more interested to have another session with the robot when their interaction included also off-activity talk, even though there was no difference in the perception of the robot by the children between the groups with and without off-activity talk. Furthermore, individual interactions with the robot positively influenced the children's adherence to a therapy-related requirement, namely the filling in of a nutritional diary.