Collaborative Research: EAGER: RI: Causal Decision-Making

人工智能（AI）在社会中扮演着越来越重要的角色，因为曾经由人类做出的决定现在被委托给自动化系统。这些系统被期望是有效的、稳健的、可解释的、可推广的，并导致社会一致同意的结果。越来越多的人认识到，强有力的决策依赖于对潜在环境的因果机制的一些了解。例如，智能机器人必须了解其环境中的因果关系，以便更稳健地规划其行动过程；医生需要了解现有药物的效果，以便为病人设计有效的策略。目前负责决策的人工智能系统并没有明确表示潜在的因果模型。该项目将为决策系统的一般框架（即一套原则、算法和工具）奠定基础，通过用因果成分丰富传统的人工智能形式主义，以实现更高效、更稳健、更可解释的决策。这项研究将为决策领域的变革埋下种子，并对开发下一代人工智能系统产生影响。随着越来越多的决策被委托给人工智能系统，研究结果预计将对人工智能基金会产生重大影响，并可能对社会产生广泛影响。研究人员将开发新的因果推理教材和课程。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人员。研究小组将继续支持“统计因果关系教育奖”，以改善统计和数据科学中现代因果推理工具的教学。该项目是将因果推理（CI）和强化学习（RL）整合到因果强化学习（CRL）学科中的第一步。这个想法是赋予RL代理一个明确的环境因果模型和新的干预和反事实推理能力。CRL将打开一个新的学习机会和挑战家庭，这是以前既不承认也不理解的。本研究的任务包括，当智能体具有不同的感知和驱动能力时，整合离线和在线方法，以及开发反事实决策的通用机制，该机制比标准的干预性决策更强大。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.