2303389
Project Grant
Overview
Grant Description
Sbir Phase I: Machine Learning Actors to Improve Connectedness Across Remote Teams -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.
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.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Methuen,
Massachusetts
01844-1579
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Bitpart Ai was awarded
Project Grant 2303389
worth $275,000
from National Science Foundation in August 2023 with work to be completed primarily in Methuen Massachusetts United States.
The grant
has a duration of 5 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Machine Learning Actors to Improve Connectedness across Remote Teams
Abstract
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. _x000D_ _x000D_ 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._x000D_ _x000D_ 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.
Topic Code
DH
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 8/3/23
Period of Performance
8/1/23
Start Date
1/31/24
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2303389
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
RLV9WDWMT877
Awardee CAGE
12ZN0
Performance District
MA-03
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $275,000 | 100% |
Modified: 8/3/23