Enabling Robots to Understand Indirect Speech Acts in Task-Based Interactions

Abstract

An important open problem for enabling truly taskable robots is the lack of task-general natural language mechanisms within cognitive robot architectures that enable robots to understand typical forms of human directives and generate appropriate responses. In this paper, we first provide experimental evidence that humans tend to phrase their directives to robots indirectly, especially in socially conventionalized contexts. We then introduce pragmatic and dialogue-based mechanisms to infer intended meanings from such indirect speech acts and demonstrate that these mechanisms can handle all indirect speech acts found in our experiment as well as other common forms of requests.