A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game
This addresses human-robot interaction in competitive settings, but it is incremental as it builds on existing game and language frameworks.
The study investigated how a robot's expressive language (encouraging vs. discouraging comments) affects human behavior and perceptions in a competitive game, finding that discouraging comments led to less rational play and more negative perceptions of the robot.
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.