Decision-Theoretic Human-Robot Interaction: Designing Reasonable and Rational Robot Behavior

Autonomous robots are moving out of research labs and factory cages into public spaces; people’s homes, workplaces, and lives. A key design challenge in this migration is how to build autonomous robots that people want to use and can safely collaborate with in undertaking complex tasks. In order for people to work closely and productively with robots, robots must behave in way that people can predict and anticipate. Robots chose their next action using the classical sense-think-act processing cycle. Robotists design actions and action choice mechanisms for robots. This design process determines robot behaviors, and how well people are able to interact with the robot. Crafting how a robot will choose its next action is critical in designing social robots for interaction and collaboration. This paper identifies reasonableness and rationality, two key concepts that are well known in Choice Theory, that can be used to guide the robot design process so that the resulting robot behaviors are easier for humans to predict, and as a result it is more enjoyable for humans to interact and collaborate. Designers can use the notions of reasonableness and rationality to design action selection mechanisms to achieve better robot designs for human-robot interaction. We show how Choice Theory can be used to prove that specific robot behaviors are reasonable and/or rational, thus providing a formal, useful and powerful design guide for developing robot behaviors that people find more intuitive, predictable and fun, resulting in more reliable and safe human-robot interaction and collaboration.