The authors combined virtual reality technology and social robotics to develop a tutoring system that resembled a small-group arrangement. This tutoring system featured a virtual teacher instructing sight words, and included a humanoid robot emulating a peer. The authors used a multiple-probe design across word sets to evaluate the effects of the instructional package on the explicit acquisition and vicarious learning of sight words instructed to three children with autism spectrum disorder (ASD) and the robot peer. Results indicated that participants acquired, maintained, and generalized 100% of the words explicitly instructed to them, made fewer errors while learning the words common between them and the robot peer, and vicariously learned 94% of the words solely instructed to the robot.

Investigation into technology-assisted intervention for children with autism spectrum disorders (ASD) has gained momentum in recent years. Research suggests that robots could be a viable means to impart skills to this population since children with ASD tend to be fascinated by robots. However, if robots are to be used to impart social skills, a primary deficit for this population, considerable attention needs to be paid to aspects of social acceptability of such robots. Currently there are no design guidelines as to how to develop socially acceptable robots to be used for intervention for children with ASD. As a first step, this work investigates social design of virtual robots for children with ASD. In this paper we describe the design of a virtual environment system for social interaction (VESSI). The design is evaluated through an innovative experiment plan that combines subjective ratings from a clinical observer with physiological responses indicative of affective states from the participants, both collected when participants engage in social tasks with the social robots in a virtual reality environment. Two social parameters of importance for this population, namely eye gaze and social distance, are systematically varied to analyze the response of the participants. The results are presented to illustrate how experiments with virtual social robots can contribute towards the development of future social robots for children with ASD.

Two main objectives in the control of Heating, Ventilating and Air Conditioning (HV AC) systems are increasing thermal comfort and reducing energy consumption. Achieving these purposes requires a suitable control system design. In this paper, a thorough review of intelligent control techniques used in HV AC systems to date is completed. Such an overview provides an insight into artificial intelligence methods for the control of HV AC systems and can offer scholars and HV AC learners comprehensive information about a variety of soft computing techniques in the field of HV AC. This information can in turn allow for improved designs of a proper controller for their work.

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Enhanced cognitive-behavioral therapy (CBT-E) is one of the primary evidence-based treatments for adults with eating disorders (EDs). However, up to 50% of individuals do not respond to CBT-E, likely because of the high heterogeneity present even within similar diagnoses. This high heterogeneity, especially in regard to presenting pathology, makes it difficult to develop a treatment based "on averages" and for clinicians to accurately pinpoint which symptoms should be targeted in treatment. As such, new models based at both the group, and individual level, are needed to more accurately refine targets for personalized evidence-based treatments that can lead to full remission. The current study (Expected N = 120 anorexia nervosa, atypical anorexia nervosa, and bulimia nervosa) will build both group and individual longitudinal models of ED behaviors, cognitions, affect, and physiology. We will collect data for 30 days utilizing a mobile application to assess behaviors, cognition, and affect and a sensor wristband that assesses physiology (heart rate, acceleration). We will also collect outcome data at 1-and 6-month follow-ups to assess ED outcomes and remission status. These data will allow for identification of "on average" and "individual" targets that maintain ED pathology and test if these targets predict outcomes, including ED remission.

In the current investigation, we evaluated the effectiveness of a multi-component package (i.e., robot, simultaneous prompting, self-graphing) for teaching three students, ages 19-21, with intellectual disabilities (ID) to write text messages that included a greeting, personal narrative, and closing. Data suggest that the package was effective in increasing correct performance for all participants. In addition, participants demonstrated their newly acquired texting skills across different communicative partners.