The affective reasoner: a process model of emotions in a multi-agent system
The affective reasoner: a process model of emotions in a multi-agent system
An effective mobile robot educator with a full-time job
Artificial Intelligence - Special issue on applications of artificial intelligence
Human conversation as a system framework: designing embodied conversational agents
Embodied conversational agents
Experiences with a mobile robotic guide for the elderly
Eighteenth national conference on Artificial intelligence
Emotion and sociable humanoid robots
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Interactions with a moody robot
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Robotics and Autonomous Systems
Imagery of disabled people within social robotics research
ICSR'12 Proceedings of the 4th international conference on Social Robotics
Users' preferences of robots for domestic use
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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For human-robot interaction to proceed in a smooth, natural manner, robots must adhere to human social norms. One such human convention is the use of expressive moods and emotions as an integral part of social interaction. Such expressions are used to convey messages such as ''I'm happy to see you'' or ''I want to be comforted,'' and people's long-term relationships depend heavily on shared emotional experiences. Thus, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot, with a focus on developing long-term human-robot relationships. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist. In addition, we present findings from two studies that demonstrate the model's potential.