The Impact of Humanoid Affect Expression on Human Behavior in a Game-Theoretic Setting

TL;DR

This study explores how a humanoid robot's affective expressions influence human decision-making in a game-theoretic context, using NLP-generated sentences to modulate human behavior in security games.

Contribution

It introduces a novel NLP-based method for robot affect expression and examines its impact on human decision-making in a game-theoretic setting.

Findings

Humans' decisions are significantly influenced by robot's affective expressions.

The NLP model effectively generates affect-specific sentences for robot communication.

Behavioral models show measurable changes based on robot mood expressions.

Abstract

With the rapid development of robot and other intelligent and autonomous agents, how a human could be influenced by a robot's expressed mood when making decisions becomes a crucial question in human-robot interaction. In this pilot study, we investigate (1) in what way a robot can express a certain mood to influence a human's decision making behavioral model; (2) how and to what extent the human will be influenced in a game theoretic setting. More specifically, we create an NLP model to generate sentences that adhere to a specific affective expression profile. We use these sentences for a humanoid robot as it plays a Stackelberg security game against a human. We investigate the behavioral model of the human player.