Briggs, GordonView (Tufts University), “Toward Dialogue and Reasoning Mechanisms to Enable More Natural and Socially-Appropriate Task-Based Human-Robot Interactions” (2016)

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Briggs, GordonView (Tufts University), “Toward Dialogue and Reasoning Mechanisms to Enable More Natural and Socially-Appropriate Task-Based Human-Robot Interactions” (2016)

Briggs, GordonView (Tufts University), “Toward Dialogue and Reasoning Mechanisms to Enable More Natural and Socially-Appropriate Task-Based Human-Robot Interactions” (2016), Advisor: Scheutz, Matthias; Goldberg, Ariel

When humans communicate with one another they utilize a variety of subtle linguistic cues that robots need to understand and reciprocate in order to achieve more natural and effective collaborative interactions with humans. The authors begin by presenting a simple human-robot interaction scenario and show that appropriate behavior by the robot requires the ability to explicitly reason about a variety of social and introspective factors that include (but are not necessarily limited to): the literal and non-literal intent of utterances, the knowledge and capability of the robot, social roles and relationships, goal priority and timing, and both norms of moral and social (e.g. politeness) varieties. Many current natural language (NL) systems used in human-robot interaction are unable modulate NL interactions appropriately given these considerations. Given this need, the authors present a novel NL understanding and generation architecture for robotic agents that provide a framework to achieve these desired capabilities. The authors begin by presenting how this architecture implements both pragmatic reasoning and reasoning regarding the belief states of interlocutors. The authors then show how this architecture can be utilized to both understand and appropriately deploy certain sentential adverbial modifiers, which enhance the informativeness and naturalness of natural language interactions. The authors also show how the architecture can not only understand non-literal requests, but also appropriately generate non-literal requests based on social context. Next, the authors demonstrate how the NL architecture is also able to generate responses to both literal and non-literal requests that conform to human sociolinguistic preferences.

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