More May Be Less: Emotional Sharing in an Autonomous Social Robot

We report a study performed with a social robot that autonomously plays a competitive game. By relying on an emotional agent architecture (using an appraisal mechanism) the robot was built with the capabilities of emotional appraisal and thus was able to express and share its emotions verbally throughout the game. Contrary to what was expected, emotional sharing in this context seemed to damage the social interaction with the users. It is commonly agreed that a social robot should be embedded with behaviours that enrich the interaction with humans, with the goal of being as natural as possible and inspired in the way we humans interact with each other. However, implementing social capabilities needs to be a cautious process, taking into account the situation, context and embodiment of the robot. In our study we created two conditions: Sharing condition (where the robot at the end of each board game shared verbally its emotions toward the game) and No Sharing (the robot did not share its emotions) and we formulated the hypothesis that participants with whom the robot shared its emotions would perceive it as more humanlike (less artificial), more close to them, and with more friendlier characteristics. Social Behaviours in HRI A way to bridge the communication gap between technology and humans, is to enhance the anthropomorphic qualities of a robot, fomenting a mutual understanding, necessary for a meaningful interaction (Duffy 2003). Various studies reinforce this perspective, showing that a robot with social behaviours affects people’s perceptions. For example, at a basic level of communication, it is found that the presence of gestures in a robot catches more the user attention than its absence (Sidner et al. 2005), or that a robot can be seen as a companion, influencing people’s perceptions of a shared event (Hoffman and Vanunu 2013). At a higher level of communication it is also found, for example, that a socially supportive robot improves children learning, comparing to a neutral robot (Saerbeck et al. 2010). A study from Kahn and colleagues(2015) even suggests the more social robots Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. become, probably, the more people will build trusting and intimate relationships with them (Kahn Jr et al. 2015). This way it can be seen, that even small social behaviours affect human-robot interaction (HRI). Also, emotions play an important role in human behaviour, contributing for the creation and maintenance of social relationships (Fischer and Manstead 2008). For this reason, they should also be taken into consideration when designing a social robot (for example, Kismet’s emotion and expression system (Breazeal 2003) or a study by Becker (2005) showing that negative empathic behaviours are also important to exist in a competitive interaction, despite the fact that they seem to reflect a less caring image toward the robot (Becker et al. 2005)). However, the context has an important effect, influencing the perceptions of these behaviours. A simple example is a study showing users cooperating more in an effortful task with a serious concerned robot, than with a playful robot (despite that they may enjoy more the playful robot) (Goetz and Kiesler 2002; Goetz, Kiesler, and Powers 2003). Kennedy, Baxter and Belpaeme (2015) also tried to implement social behaviours in a robot and found these to affect negatively learning improvements in a task with a social robotic tutor, compared to a non social one (Kennedy, Baxter, and Belpaeme 2015). Goetz, Kiesler and Powers (2003) studies show that people expect the robot to look and act accordingly to the task context, increasing their compliance with it. (Goetz, Kiesler, and Powers 2003). All of this reinforces the need to test and be cautious when implementing these behaviours. An Autonomous Social Robot that Shares Emotions In this research we created a system for a social robot (the Emys robot) that tries to embed two components (decision making/playing and social) and explore the impact that these components have in the perceptions of the users. The game considered is a variant of the dots and boxes game (Berlekamp 2000), called Coins and Strings. Players take turns removing strings. The player who removes the last string attatched to a coin collects the coin and will play again. The game ends when all strings are removed, and the player with the highest number of coins wins the game. To create a social part of the robot in the context of a competiArtificial Intelligence for Human-Robot Interaction Papers from the AAAI 2015 Fall Symposium