RI: Small: Cooperative Planning and Learning via Scalable and Learnable Multi-Agent Commitments

Stemming from human societies, the notion of commitment refers to a decision maker, or agent, making credible and prolonged promises about various aspects of the consequences of its future decisions, thus facilitating cooperation with other agents. Engineering commitments is therefore a promising framework to achieve cooperative artificial intelligence (AI) that equips a group of autonomous agents with the capability of planning and learning to maximize their joint utility. This research project seeks to initiate a paradigm shift that brings the notion of commitment to its full potential by scaling it to various dimensions of complexity in cooperative AI, developing novel methods that promise to significantly and positively impact real-world and large-scale cooperative AI applications. This project integrates an array of education initiatives, playing key roles in PI's classes, the recruitment and training of undergraduate students from underrepresented backgrounds, and extensive activities planned to involve high school students and junior researchers.

This research consists of two cohesive thrusts: Thrust 1 redesigns an existing approach for commitment-based distributed cooperative planning with a predefined parameterization for probabilistic commitments, by developing novel algorithms and analyses in planning under constraints and uncertainty, approximate linear programming, and robust planning that address long decision horizon and high-dimensional perception and action; Thrust 2 develops and evaluates a novel approach for distributed cooperative learning with emergent commitment parameterization, which combines the best from the framework of multi-agent commitments and deep reinforcement learning to address all aforementioned dimensions of complexity. Success of the proposed research is expected to significantly increase the applicability of commitment-based planning and learning for large-scale and complex cooperative AI systems.

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.

Award Number: 2544947
Principal Investigator: Qi Zhang
Funds Obligated: $170,544
State: MA