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）在社会中发挥着越来越重要的作用，因为曾经由人类做出的决策现在被委托给自动化系统，这些系统预计将是高效、稳健、可解释、可推广的，并产生社会认可的结果。人们越来越认识到，稳健的决策依赖于对环境背后因果机制的了解，例如，智能机器人必须了解其环境中的因果关系，才能更稳健地规划其行动方案。需要了解可用药物的作用为她的患者设计有效的策略。当前一代负责决策的人工智能系统并未明确代表底层因果模型，该项目将为通用框架（即一组原则、算法和工具）奠定基础。 - 通过用因果成分丰富传统人工智能形式，实现更高效、稳健和可解释的决策，该研究将为决策领域的变革播下种子，并对下一代的发展产生影响。预计人工智能系统的研究成果。随着越来越多的决策被委托给人工智能系统，研究人员将开发新的因果推理教育材料和课程，并对人工智能基础产生重大影响，并可能对社会产生广泛影响。致力于继续从代表性不足的群体中招募人才，研究团队将继续支持“统计教育中的因果关系奖”，以改善统计和数据科学领域现代因果推理工具的教学。该项目是迈向整合的第一步。因果推理（CI）和强化学习（RL）进入因果强化学习（CRL）学科，其想法是赋予 RL 智能体一个明确的环境因果模型以及新的干预和反事实推理能力。这项研究中包含的一系列学习机会和挑战以前既没有被承认也没有被理解，包括当代理具有不同的感知和执行能力时整合离线和在线方法，以及开发比事实更强大的通用机制。其标准、介入该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。