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

RI：小型：通过可扩展和可学习的多代理承诺进行合作规划和学习

• 批准号: 2154904
• 负责人: Qi Zhang
• 金额: $ 33.24万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154904&HistoricalAwards=false
• 关键词: RI; Small; Cooperative; Planning; Learning

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.

承诺的概念源于人类社会，是指决策者或代理人对其未来决策后果的各个方面做出可信且长期的承诺，从而促进与其他代理人的合作。因此，工程承诺是实现协作人工智能（AI）的一个有前途的框架，它使一组自主代理具有规划和学习的能力，以最大化其联合效用。该研究项目旨在发起一种范式转变，通过将承诺概念扩展到合作人工智能的复杂性的各个维度，开发有望对现实世界和大规模合作人工智能产生重大和积极影响的新方法，从而充分发挥承诺概念的潜力。应用程序。该项目整合了一系列教育举措，在 PI 课程中发挥关键作用，招募和培训来自弱势背景的本科生，以及计划让高中生和初级研究人员参与的广泛活动。这项研究由两个有凝聚力的主旨组成： 主旨 1重新设计基于承诺的分布式协作规划的现有方法，通过在约束和不确定性下开发新颖的算法和规划分析、近似线性规划以及解决长决策范围和问题的鲁棒规划，对概率承诺进行预定义参数化。高维度的感知和行动； Thrust 2 开发并评估了一种具有紧急承诺参数化的分布式协作学习新方法，该方法结合了多智能体承诺和深度强化学习框架的最佳优点，以解决上述所有复杂性维度。拟议研究的成功预计将显着提高基于承诺的规划和学习对大规模和复杂的合作人工智能系统的适用性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和能力进行评估，被认为值得支持。更广泛的影响审查标准。

项目成果

Risk-aware analysis for interpretations of probabilistic achievement and maintenance commitments

用于解释概率成就和维护承诺的风险意识分析

• DOI: 10.1016/j.artint.2023.103864
• 发表时间: 2023-04
• 期刊: Artificial Intelligence
• 影响因子: 14.4
• 作者: Zhang, Qi;Durfee, Edmund H.;Singh, Satinder
• 通讯作者: Singh, Satinder

Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

用于协作多智能体强化学习的上下文感知贝叶斯网络演员批评家方法

• DOI: 10.48550/arxiv.2306.01920
• 发表时间: 2023-06-02
• 期刊: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Dingyang Chen;Qi Zhang
• 通讯作者: Qi Zhang