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经无处不在，决策作为一项科学挑战的重要性也急剧增加。曾经由人类做出的决策越来越多地委托给自动化系统或在自动化系统的协助下做出。然而，尽管最近取得了实质性进展，但当前一代人工智能技术缺乏可解释性、鲁棒性和适应性能力，这阻碍了人们对人工智能的信任。人们越来越认识到，稳健的决策需要了解环境背后通常复杂且动态的因果机制，而当前人工智能的大多数形式主义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和学习的力量，以产生依赖较少数据的人工智能系统，可以更好地向人们证明和解释他们的决策，更好地对新情况做出反应，从而更安全、更值得信赖。该项目为因果决策系统提供了新的基础——原则、方法和工具。它用因果成分丰富了传统的人工智能形式，以实现更高效、稳健、可概括和可解释的决策，并有可能从根本上改变人工智能决策领域。该理论将通过机器人和公共卫生领域的现实用例进行评估。研究人员将进行广泛的教育工作，并开发培训内容，重点是指导和扩大代表性不足群体的参与。该团队将从事知识转移活动，包括撰写一本关于因果决策的介绍性书籍以及组织讨论人工智能和决策主题的活动。该项目将结构因果模型的框架与人工智能决策的领先方法相结合，包括基于模型的规划与马尔可夫决策过程及其扩展、强化学习和图形模型（例如影响图）。结果通过因果建模彻底改变了传统的人工智能决策，以开发更高效、稳健、可概括和可解释的决策系统。该项目通过三个主旨开发了新的基础（即原理、理论和算法），并为因果授权决策提供了一个通用的统一框架，概括了领先的决策方法。 Thrust 1 研究因果决策的基本方面，以保证自主代理和决策支持系统的决策稳健、样本高效且精确。这些目标是通过开发因果整合的在线和离线政策学习、干预计划、模仿学习、课程学习、知识转移和适应的方法来实现的。 Thrust 2 研究了因果决策的其他方面，这些方面对于人类参与循环的决策支持系统尤其重要，包括如何利用因果关系来构建解释、决定何时让人类参与以及赋予系统能力意识和能力。能够做出符合用户价值观的公平决策。 Thrust 3 增强了所得工具的可扩展性及其高效推理的能力，在多个目标之间以及可解释性和决策质量之间进行权衡，并学习世界的因果模型。这些推动力将共同促进新一代强大的人工智能工具的开发，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。