SBIR Phase I: Machine Learning Actors to Improve Connectedness across Remote Teams

Award ID: 2303389

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop machine learning-powered actors (ML actors) that facilitate social encounters between friends, strangers, classmates, and coworkers in user-generated spaces across the Metaverse. The shift towards virtual work, learning, and socialization has been accompanied by significant societal disruption. Over the past few years, people across the United States reported increasing levels of loneliness and isolation. Building off research that shows games are a powerful tool for team building, and non-player characters have a significant impact on building empathy, this project uses ML actors as the building blocks of free-to-play, multiplayer, cooperative games designed to bring remote workers together socially. This Small Business Innovation Research (SBIR) Phase I project aims to address the challenge of making ML actors viable for user-generated worlds. In order to be effective in the Metaverse, ML actors will need to navigate unfamiliar settings, player dialogue, and behaviors that are hard to predict. Characters will need to be trained on vast quantities of data with some human supervision. This project seeks to prove that ML actors can be trained from large amounts of data by users of no technical background and those actors can then be deployed in a virtual environment in which they are responsive to their environment and player choices. This project has three main steps: 1) learning a large multimodal hierarchical task network from thousands of movie scripts and game logs, 2) connecting that model to a character in a 3D environment, and 3) testing a game with remote teams to gauge efficacy and enjoyability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk