Social intelligence is the ability to interact effectively with others in order to accomplish your goals (Ford & Tisak, 1983). Social intelligence is critically important for social robots, which are designed to interact and communicate with humans (Dautenhahn, 2007). Social robots might have goals such as building relationships with people, teaching people, learning something from people, helping people accomplish tasks, and completing tasks that directly involve people’s bodies (e.g., lifting people, washing people) or minds (e.g., retrieving phone numbers for people, scheduling appointments for people). In addition, social robots may try to avoid interfering with tasks that are being done by people. For example, they may try to be unobtrusive and not interrupt.

Social intelligence is also important for robots engaged in non-social tasks if they will be around people when they are doing their work. Like social robots, such task-focused robots may be designed to avoid interfering with the work of people around them. This is important not just for the people the robots work with, but also for the robots themselves. For example, if a robotic vacuum bumps into people or scares household pets, the owners may turn it off. In addition, task-focused robots will be better able to accomplish their goals if they can inspire people to assist them when needed. For example, if a delivery robot is trying to take a meal to a certain room in a hospital and its path is blocked by a cart, it may be beneficial if it can inspire nearby humans to move the cart.

While previous research on human-robot interaction (HRI) has referenced and contained aspects of the social intelligence of robots (Bartneck, Kulic, Croft, & Zoghbi, 2009; Ho, MacDorman, 2010; Ho, MacDorman, 2017; Moshkina, 2012; Nomura, Suzuki, Kanda, & Kato, 2006), the concept of robotic social intelligence has not been clearly defined. Measures of similar concepts are brief and incomplete, and often include extraneous variables. Moreover, measures of human social intelligence (e.g., Baron-Cohen, S. Wheelwright ,& Hill, 2001; Silvera, Martinussen, & Dahl, 2001) cannot be adapted for robots, because they assess skills that current and near-future robots do not have and because they omit basic skills that are essential for smooth social interactions. Therefore, we designed a set of 20 scales to measure the perceived social intelligence of robots. See the Appendix. This document explains how these scales were developed and how they can be used.

@techreport{barchard2018perceived,
  title={Perceived Social Intelligence (PSI) Scales test manual. Unpublished psychological test and test manual. Observer report of 20 aspects of social intelligence of robots, with four items per scale},
  author={Kimberly Barchard and Leiszle Lapping-Carr and Shane Westfall and Santosh Balajee Banisetty and David Feil-Seifer},
  year={2018},
  month={August},
  publisher={Available from Kim Barchard, kim.barchard@unlv.edu and David Feil-Seifer, dave@cse.unr.edu},
}

CHS: Small: Socially-Aware Navigation, National Science Foundation PI: David Feil-Seifer, co-PI: Monica Nicolescu, Amount: $500,000, Sept. 1, 2017 - May 31, 2022

Infrastructure For Socially-Aware Navigation For Long-Term Human-Robot Interaction, NASA EPSCoR PI: David Feil-Seifer, co-PI: Kimberly Barchard, Amount: $30,416, July 1, 2017 - April 9, 2018