Collaborative Research: EAGER: RI: Causal Decision-Making

协作研究：EAGER：RI：因果决策

• 批准号: 2231796
• 负责人: Elias Bareinboim
• 金额: $ 15万
• 依托单位: Columbia University
• 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: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231796&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; RI; Causal

Artificial intelligence (AI) plays an increasingly prominent role in society since decisions that were once made by humans are now being delegated to automated systems. These systems are expected to be efficient, robust, explainable, generalizable, and lead to outcomes agreed upon by society. There is a growing understanding that robust decision-making relies on some knowledge of the causal mechanisms underlying the environment. For instance, an intelligent robot has to know the cause and effect relationships in its environment to plan its course of action more robustly; a physician needs to understand the effects of available drugs to design an effective strategy for her patients. The current generation of AI systems responsible for decision-making does not explicitly represent the underlying causal model. This project will build the foundations toward a general framework — i.e., a set of principles, algorithms, and tools — for decision-making systems by enriching the traditional AI formalism with causal ingredients for more efficient, robust, and explainable decision-making. The research will plant the seed for a transformation in the decision-making field and have consequences for developing the next generation of AI systems. The research results are expected to have significant impacts on AI foundations and may potentially have broad implications for society as more and more decisions are being delegated to AI systems. The researchers will develop new educational materials and course curricula in causal inference. The researchers will provide research training for graduate students and are committed to continuing to recruit from underrepresented groups. The research team will continue supporting the “Causality in Statistics Education Award” to improve the teaching and learning of modern causal inference tools in statistics and the data sciences.This project is the first step toward the integration of causal inference (CI) and reinforcement learning (RL) into the discipline of causal reinforcement learning (CRL). The idea is to endow an RL agent with an explicit causal model of the environment and new capabilities for interventional and counterfactual reasoning. CRL will open a new family of learning opportunities and challenges that were neither acknowledged nor understood before. The tasks included in this research include integrating offline and online methods when the agents have different perceptual and actuation capabilities and developing general machinery for counterfactual decision-making, which is more powerful than its standard, interventional counterpart.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）在社会中起着越来越重要的作用，因为曾经由人类做出的决定已被委派给自动系统。预计这些系统将有效，健壮，解释，可推广，并导致社会达成的结果。越来越多的理解是，强有力的决策依赖于对环境背后的因果机制的一些知识。例如，智能机器人必须知道其环境中的因果关系，以更加牢固地计划其行动方案。身体需要了解可用药物对患者设计有效策略的影响。当前负责决策的AI系统并不能明确表示基本的催化模型。该项目将通过通过因果归纳丰富传统的AI格式，以更有效，健壮，可解释的决策来建立一个通用框架（即一组原理，算法和工具），以建立基础。这项研究将种植在决策领域进行转变的种子，并对开发下一代AI系统产生后果。预计该研究结果将对AI基础产生重大影响，并且可能会对社会产生广泛的影响，因为越来越多的决策被委派给了AI系统。研究人员将开发新的教育材料和课程课程。研究人员将为研究生提供研究培训，并致力于继续从代表性不足的群体中招募。研究团队将继续支持“统计学教育奖的因果关系”，以改善统计和数据科学中现代分类推理工具的教学和学习。该项目是迈向分类推理（CI）和加强学习（RL）迈向因果增强学习学科（CRL）的第一步。这个想法是赋予RL代理具有明确的环境因果模型，并具有新的介入和反事实推理的能力。 CRL将开设一个新的学习机会和挑战家族，以前被承认或理解。 The tasks included in this research include integrating offline and online methods when the agents have different perceptual and activation capabilities and developing general machinery for counterfactual decision-making, which is more powerful than its standard, interventional counterpart.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.