Collaborative Research: III: Medium: VirtualLab: Integrating Deep Graph Learning and Causal Inference for Multi-Agent Dynamical Systems

许多现实世界的领域，包括物理系统、社会系统、脑网络和传染病网络，都可以被概念化为多智能体动态系统，其中不同的智能体相互作用并根据特定的动态进行进展。了解和模拟这些系统可以增强我们对其潜在机制的理解，使我们能够在有或没有干预的情况下做出更准确的长期预测和更知情的决策。尽管对特定领域的多智能体动力系统进行了广泛的研究，但目前还没有通用的解决方案，即使是最有知识的专家也可能难以从数学上描述它们。拟议的VIRTUALLAB框架旨在创建一个虚拟实验室，能够从观察到的数据学习系统动力学，预测未来的代理轨迹，并准确预测一系列干预措施下的潜在系统结果。该项目将促进人工智能技术在不同领域的快速采用，推动数字革命，并将人工智能用于医疗保健、科学和公共政策。研究人员计划将教育活动纳入研究，为学生提供令人兴奋的机会，将人工智能和ML应用于生物医学研究、材料科学和公共卫生等各个领域。他们还将通过出版物、各种会议的教程以及与领域专家的合作来广泛传播他们的发现。该项目已经确定了现有建模和预测多智能体动态系统的方法中的几个局限性。首先，方法通常是特定于领域的，并且缺乏解决整个动力系统范围的通用方法。其次，大多数动力系统都是由复杂的常微分方程式或偏微分方程式来定义的，这些方程式很难甚至不可能设计出来。第三，预测可能非常耗时，而且可能不适用于大型系统。最后，很少有工作涉及多智能体系统中的因果推理问题。VIRTUALLAB框架旨在实现变革并应对这些挑战。首先，它将提供在广泛的应用中对多智能体动态系统进行建模的一般解决方案，其中动态可以从来自相同或相关系统的不完整和不规则的观测数据中学习。这将涉及解决几个挑战，例如根据不完整的信号建立连续动力学模型，设计捕获高阶非线性动力学的模型，将学习的动力学推广到几乎没有观测的新系统，以及缩放模型以处理大规模系统并使现实世界系统的训练和推理高效。其次，VIRTUALLAB将通过利用离线数据，在节点或系统级别提供对干预后潜在结果的准确预测。这样做将涉及处理系统级和节点级干预以及连续时间动态干预，而不是未来可能发生的静态干预。最后，该项目将使用几个用例来测试和评估建议的框架，包括功能大脑网络、分子动力学和流行病动力学。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many real-world domains, spanning physical systems, social systems, brain networks, and epidemic networks, can be conceptualized as multi-agent dynamic systems, wherein different agents interact with each other and progress according to specific dynamics. Understanding and modeling these systems can enhance our comprehension of their underlying mechanisms, allowing us to make more accurate long-term predictions and better-informed decisions, with or without interventions. Despite extensive study of multi-agent dynamical systems in specific domains, there is currently no general solution available, and even the most knowledgeable experts may struggle to describe them mathematically. The proposed VIRTUALLAB framework aims to create a virtual lab capable of learning system dynamics from observed data, predicting future agent trajectories, and accurately forecasting potential system outcomes under a range of interventions. This project will facilitate the rapid adoption of AI techniques in different domains, promoting the digital revolution and the use of AI for healthcare, science, and public policy. The investigators plan to incorporate educational activities into the research, offering students exciting opportunities to apply AI and ML in various domains such as biomedical research, material science, and public health. They will also widely disseminate their findings through publications, tutorials at various conferences, and collaborations with domain experts. The project has identified several limitations in existing approaches to modeling and predicting multi-agent dynamical systems. Firstly, approaches are often domain specific, and there is a lack of general methodology to address the full range of dynamical systems. Secondly, most dynamical systems are defined by complex ordinary or partial differential equations that can be difficult or even impossible to devise. Thirdly, making predictions can be very time-consuming and may not be applicable to large-scale systems. Lastly, very little work has addressed the problem of causal inference in multi-agent systems. The VIRTUALLAB framework is designed to be transformative and address these challenges. Firstly, it will provide general solutions to model multi-agent dynamical systems across a broad spectrum of applications, where the dynamics can be learned from incomplete and irregular observational data from the same or related systems. This will involve addressing several challenges, such as modeling continuous dynamics from incomplete signals, designing models that capture high-order nonlinear dynamics, generalizing learned dynamics to new systems with few observations, and scaling models to handle large-scale systems and make training and inference efficient for real-world systems. Secondly, VIRTUALLAB will provide accurate predictions of potential outcomes after an intervention, either at the node or system level, by leveraging offline data. Doing so will involve handling both system- and node-level intervention and continuous-time dynamic intervention, rather than static intervention that may occur in the future. Lastly, the project will test and evaluate the proposed framework using several use cases, including functional brain networks, molecular dynamics, and epidemic dynamics.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.