Uncertainty-Resolving Questions for Social Robots

Abstract

Social robots should deal with uncertainties in unseen environments and situations in an interactive setting. For humans, questionanswering 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. © 2023 IEEE Computer Society. All rights reserved.