Using General-Purpose Planning for Action Selection in Human-Robot Interaction

Abstract

A central problem in designing and implementing interactive systems—action selection—is also a core research topic in automated planning. While numerous toolkits are available for building end-to-end interactive systems, the tight coupling of representation, reasoning, and technical frameworks found in these toolkits often makes it difficult to compare or change the underlying domain models. In contrast, the automated planning community provides general-purpose representation languages and multiple planning engines that support these languages. We describe our recent work on automated planning for task-based social interaction, using a robot that must interact with multiple humans in a bartending domain.