Exploring Data-Driven Components of Socially Intelligent AI through Cooperative Game Paradigms

Abstract

The development of new approaches for creating more “life-like” artificial intelligence (AI) capable of natural social interaction is of interest to a number of scientific fields, from virtual reality to human–robot interaction to natural language speech systems. Yet how such “Social AI” agents might be manifested remains an open question. Previous research has shown that both behavioral factors related to the artificial agent itself as well as contextual factors beyond the agent (i.e., interaction context) play a critical role in how people perceive interactions with interactive technology. As such, there is a need for customizable agents and customizable environments that allow us to explore both sides in a simultaneous manner. To that end, we describe here the development of a cooperative game environment and Social AI using a data-driven approach, which allows us to simultaneously manipulate different components of the social interaction (both behavioral and contextual). We conducted multiple human–human and human–AI interaction experiments to better understand the components necessary for creation of a Social AI virtual avatar capable of autonomously speaking and interacting with humans in multiple languages during cooperative gameplay (in this case, a social survival video game) in context-relevant ways.

Keywords

• human–robot interaction
• social cognition
• cooperative games
• speech systems
• virtual avatar
• autonomous agents