Affective computing
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
The advisor robot: tracing people's mental model from a robot's physical attributes
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Robot deception: recognizing when a robot should deceive
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
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This paper explores the use of an outcome matrix as a computational representation of social interaction suitable for implementation on a robot. An outcome matrix expresses the reward afforded to each interacting individual with respect to pairs of potential behaviors. We detail the use of the outcome matrix as a representation of interaction in social psychology and game theory, discuss the need for modeling the robot's interactive partner, and contribute an algorithm for creating outcome matrices from perceptual information. Experimental results explore the use of the algorithm with different types of partners and in different environments.