Uncertainty-Resolving Questions for Social Robots

Abstract

Social robots should deal with uncertainties in unseen environments and situations in an interactive setting. For humans, question-answering is one of the most typical activities for resolving or reducing uncertainty by acquiring additional information, which is also desirable for social robots. In this study, we propose a framework for leveraging the research on learning-by-asking techniques for social robots. This framework is inspired by human inquiries. Information seeking by asking should be considered at the multi-dimensional level, including required knowledge, cognitive processes, and question types. These dimensions offer a framework to embed generated questions into the three-dimensional question space, which is expected to provide a reasonable benchmark for the active learning approach and evaluation methodologies of uncertainty-resolving question generation for social robots.