Project Grant 2154904

Award Date 7/1/22
Completion Date 6/30/25
Dollars Obligated $665K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Columbia, SC 29208, USA
Similar Awards
This $1,500,000 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) supports the University of Southern California's research on safe multi-agent systems using a neurosymbolic approach. The project aims to develop new theories and algorithms for the design of safe learning-enabled multi-agent systems, with applications in areas like wildfire prevention using drone swarms and semi-automated...
This National Science Foundation Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $467,141 to the University of Georgia Research Foundation to investigate decision-making frameworks for open multi-agent systems with various forms of uncertainty. The research aims to develop novel planning and reinforcement learning techniques to enable agents to operate optimally in open contexts where the system composition, tasks, and agent...
This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems supports research to significantly increase the scalability of algorithms for solving large-scale, multi-step, imperfect-information strategic interactions. Specifically, the $854,896 award to Carnegie Mellon University (CMU) from August 1, 2023 to July 31, 2026 will fund the design, implementation, and testing of novel techniques in three main areas: Scalable subtree solving...
This $398,990 federal Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research exploring fundamental principles underlying multi-agent learning and interactions in complex systems. The primary goals are to develop computationally efficient algorithms that provably converge to equilibrium states, particularly in scenarios with both cooperative and competitive agent interactions and large action...
The University of South Carolina received a $139,999 project grant award from the National Science Foundation to advance neuro-symbolic artificial intelligence with deep knowledge-infused learning from July 1, 2021 to June 30, 2023. The grant supports research under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which aims to support investigator-initiated research and education in computing, communications, and information science and engineering....
This EAGER: NAIRR PILOT federal project grant, awarded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), provides $150,000 to the University of South Carolina to investigate the development of a specialized AI model for planning-like tasks. The project aims to create a comprehensive, yet compact, AI foundation model that can outperform and be more efficient and understandable than current large language models when applied...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award, with an ID of 2339844 and a total funding amount of $527,281, supports research to develop novel algorithms for solving complex real-world planning and decision-making problems. The project aims to create new algorithms with stronger guarantees than previous approaches, enabling solutions to otherwise intractable tasks in areas such as statistical inference, structured learning,...
This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems, under the NSF CISE (Computer and Information Science and Engineering) program (CFDA 47.070), provides $424,916 to the Board of Regents of the University of Nebraska (dba University of Nebraska) from August 1, 2023 to July 31, 2027. The award supports collaborative research to investigate decision-making frameworks for open multiagent systems, where agents may enter/leave the...
This National Science Foundation (NSF) Division of Information and Intelligent Systems Project Grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $299,214 over 4 years to Oberlin College to conduct collaborative research on decision-making methods for open multi-agent systems. The research investigates how autonomous agents can make optimal decisions under various types of uncertainty, including changes to the system composition, tasks,...
This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program grant, awarded under CFDA 47.070, supports the University of Utah's research project titled "SLES: HIGH-CONFIDENCE GUARANTEES FOR SAFE REWARD AND POLICY LEARNING UNDER UNCERTAINTY." The $439,425 award, effective August 15, 2024 through July 31, 2027, aims to develop scalable learning methods that are robust to uncertainty, enable self-assessment, and provide test cases for assessing...

This National Science Foundation project grant award of $664,846 provides funding from July 1, 2022 to June 30, 2025 to support research at the University of South Carolina under the Computer and Information Science and Engineering program (CFDA 47.070).

The award will support the development of novel methods for scalable and learnable multi-agent commitments to facilitate cooperative artificial intelligence planning and learning. The research consists of two thrusts. The first will redesign an existing commitment-based distributed cooperative planning approach with probabilistic commitments by developing algorithms for planning under uncertainty and constraints. The second will develop and evaluate an emergent commitment parameterization approach combining multi-agent commitments and deep reinforcement learning.

The goal is to significantly increase the applicability of commitment-based planning and learning for large-scale, complex cooperative AI systems. The award also incorporates education initiatives involving the principal investigator's classes and recruitment and training of underrepresented undergraduate students, as well as activities engaging high school students and junior researchers.

Generated 1/7/24, 2:05 AM