Can a robot understand a human’s social behavior? Moreover, how should a robot act in response to a human’s behavior? If the goals of artificial intelligence are to understand, imitate, and interact with human level intelligence then researchers must also explore the social underpinnings of this intellect. Our endeavor is buttressed by work in biology, neuroscience, social psychology and sociology. Initially developed by Kelley and Thibaut, social psychology’s interdependence theory serves as a conceptual skeleton for the study of social situations, a computational process of social deliberation, and relationships (Kelley & Thibaut, 1978). We extend and expand their original work to explore the challenge of interaction with an embodied, situated robot.

This dissertation investigates the use of outcome matrices as a means for computationally representing a robot’s interactions. We develop algorithms that allow a robot to create these outcome matrices from perceptual information and then to use them to reason about the characteristics of their interactive partner. This work goes on to introduce algorithms that afford a means for reasoning about a robot’s relationships and the trustworthiness of a robot’s partners. Overall, this dissertation embodies a general, principled approach to human-robot interaction which results in a novel and scientifically meaningful approach to topics such as trust and relationships.