CISE: Large: Causal Foundations for Decision Making and Learning

CISE：大型：决策和学习的因果基础

• 批准号: 2321786
• 负责人: Elias Bareinboim
• 金额: $ 500万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321786&HistoricalAwards=false
• 关键词: CISE; Large; Causal; Foundations; Decision

Artificial intelligence (AI) has become ubiquitous in our daily lives, and the importance of decision-making as a scientific challenge has increased dramatically. Decisions that were once made by humans are increasingly delegated to automated systems or made with their assistance. However, despite substantial recent progress, the current generation of AI technology is lacking in explainability, robustness, and adaptability capabilities, which hinders trust in AI. There is a growing recognition that robust decision-making requires an understanding of the often complex and dynamic causal mechanisms underlying the environment, while most of the current formalisms in AI lack explicit treatment of causal mechanisms. This project brings together the power of causal modeling and AI decision-making and learning to produce AI systems that rely on less data, can better justify and explain their decisions to people, better react to new circumstances, and consequently are safer and more trustworthy. The project produces new foundations - principles, methods, and tools - for causal decision-making systems. It enriches the traditional AI formalisms with causal ingredients for more efficient, robust, generalizable, and explainable decision-making with the potential to fundamentally transform the AI decision-making field. The theory will be evaluated through real-world use-cases in robotics and public health. The researchers will make extensive educational efforts, and develop training content with a focus on mentorship and broadening the participation of underrepresented groups. The team will engage in knowledge transfer activities including authoring an introductory book on causal decision-making and organizing events to discuss AI and decision-making topics.This project integrates the framework of structural causal models with the leading approaches for decision-making in AI, including model-based planning with Markov decision processes and their extensions, reinforcement learning, and graphical models such as influence diagrams. The outcomes revolutionize traditional AI decision-making with causal modeling toward developing more efficient, robust, generalizable, and explainable decision-making systems. In three thrusts, the project develops new foundations (i.e., principles, theory, and algorithms) and provides a common unified framework for causal-empowered decision-making that generalizes the leading decision-making approaches. Thrust 1 studies essential aspects of causal decision-making to guarantee that the decisions of autonomous agents and decision-support systems are robust, sample-efficient, and precise. These goals are realized by developing methods for causality-integrated online and offline policy learning, interventional planning, imitation learning, curriculum learning, knowledge transfer, and adaptation. Thrust 2 studies additional aspects of causal decision-making that are especially important for decision-support systems where humans are in the loop, including how to exploit causality for constructing explanations, decide when to involve humans, and endow the systems with competence awareness and the ability to make fair decisions that align with the values of their users. Thrust 3 enhances the scalability of the resulting tools and their ability to reason efficiently, trade-off between both multiple objectives and between explainability and decision quality, and learn a causal model of the world. Together, these thrusts will contribute to a new generation of powerful AI tools for developing autonomous agents and decision-support 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.

人工智能（AI）在我们的日常生活中已变得无处不在，决策作为科学挑战的重要性已大大增加。曾经由人类做出的决定越来越多地委派给自动化系统或在其协助下做出的决定。但是，尽管最近取得了长足的进步，但目前的AI技术缺乏解释性，鲁棒性和适应性能力，这阻碍了对AI的信任。越来越多的认识是，强大的决策需要了解环境中经常复杂而动态的因果机制，而人工智能中当前的大多数形式主义都缺乏对因果机制的明确处理。该项目汇集了因果建模和人工智能决策的力量以及学习生产依赖数据较少数据的AI系统，可以更好地证明和解释他们的决策，对新情况做出更好的反应，因此更安全，更值得信赖。该项目为因果决策系统生成了新的基础 - 原理，方法和工具。它丰富了传统的AI形式主义，具有因果成分，以提高效率，健壮，可解释的决策，并有可能从根本上改变AI决策领域。该理论将通过机器人技术和公共卫生中的现实世界用例来评估。研究人员将做出广泛的教育工作，并开发培训内容，重点是指导和扩大代表性不足的群体的参与。该团队将从事知识转移活动，包括撰写有关因果决策和组织活动的介绍性书，以讨论AI和决策主题。本项目将结构性因果模型的框架与AI中的决策框架相结合，包括基于模型的计划与Markov决策过程及其分类，强化学习，以及图形模型，例如影响模型。这些结果通过因果建模彻底改变了传统的AI决策，以发展更有效，健壮，可解释的决策系统。在三个推力中，该项目开发了新的基础（即原则，理论和算法），并为因果关系的决策提供了一个共同的统一框架，从而推广了领先的决策方法。推力1研究因果决策的基本方面，以确保自主药物和决策支持系统的决策具有牢固，样本效率和精确性。这些目标是通过开发因果关系在线和离线政策学习，介入计划，模仿学习，课程学习，知识转移和适应方法来实现的。推力2研究因果决策的其他方面，对于人类在循环中的决策支持系统中尤为重要，包括如何利用因果关系来构建解释，决定何时参与人类，并赋予系统能力意识以及与用户价值观保持公平决策的能力。推力3增强了所得工具的可扩展性及其有效推理，多个目标之间以及解释性和决策质量之间的权衡以及学习世界因果模型的能力。这些推力将共同有助于新一代强大的AI工具，用于开发自主代理和决策支持系统。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。