A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game

TL;DR

This study investigates how a robot's expressive language influences human strategies and perceptions in a competitive game, revealing that discouraging comments lead to less rational play and more negative perceptions.

Contribution

It introduces a novel experimental framework for studying human-robot interactions in competitive settings and provides an open source NLP tool for generating expressive robot speech.

Findings

Discouraging robot comments reduce human rationality.

Humans perceive robots more negatively when they use discouraging language.

Expressive language significantly impacts human strategy and perception in competitive games.

Abstract

As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of "encouraging" or "discouraging" verbal commentary) and measure the impact on participants' rationality, strategy prioritization, mood, and perceptions of the robot. We learn that a robot opponent that makes discouraging comments causes a human to play a game less rationally and to perceive the robot more negatively. We also contribute a simple open source Natural Language Processing framework for generating expressive sentences, which was used to generate the speech of our autonomous social robot.