CRII: III: Robust and Explainable AI Agents with Common Sense

CRII：III：具有常识的鲁棒且可解释的人工智能代理

• 批准号: 2153546
• 负责人: Filip Ilievski
• 金额: $ 17.5万
• 依托单位: University of Southern California
• 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: 2022-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153546&HistoricalAwards=false
• 关键词: CRII; III; Robust; Explainable; AI

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project will gain an understanding of how to create Artificial Intelligence (AI) agents that provide commonsense explanations about real-world narratives. Current AI agents lack commonsense mechanisms to explain their judgment of everyday stories and they cannot be applied to novel scenarios. This award will enable AI agents to reason in novel situations and to explain their decisions. The project will focus on two key aspects of stories: understanding situations and judging the adequacy of actions in context. The project will test the ability of AI agents to complete narratives and to provide commonsense explanations on the task of explainable natural language inference. The explainability of AI agents can be expected to improve public trust in AI technologies. Robust and explainable AI with common sense is also critically missing in social AI assistants that aim to increase the participation of children with Autism Spectrum Disorder and the elderly with Alzheimer's dementia. The investigator will design a new set of lectures and a full course on the topic of “AI assistants with common sense”, which will be taught both at USC as well as internationally. Interdisciplinary research will be facilitated via summer internships, and participation in the existing University of Southern California (USC) Center for Knowledge-Powered Interdisciplinary Data Science and NSF Research Experiences for Undergraduates programs. The investigator will partner with USC's Center for Engineering Diversity and Women in Science and Engineering, in order to recruit members of historically underrepresented groups for research on this project. The investigator will partner with USC's K-12 STEM Center to engage K-12 students from historically underrepresented groups.This award will create a paradigm shift in the development of AI agents, by combining advances in neural language modeling with high-level explanations based on logical axioms and commonsense knowledge. State-of-the-art technology is not adequate for this goal: neural methods cannot infer causal links between events and the motivations and goals of the agents directly from narratives, whereas commonsense axioms and knowledge resources alone cannot handle the contextual variations in human language. The team of researchers will build AI agents that use common sense to explain their reasoning. To do so, the researchers will leverage commonsense knowledge and axioms about agent psychology and event causality in order to enrich story corpora. The enriched data will be used to pre-train neuro-symbolic agents to complete open-world narratives and justify their completion with commonsense explanations. The researchers will measure the impact of representative techniques, axiomatic theories, and knowledge dimensions on understanding narratives about situations and actions.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。该项目将了解如何创建人工智能（AI）代理，从而提供有关现实世界叙事的常识性解释。当前的AI代理缺乏常识性机制来解释他们每天的故事的法官，并且无法应用于新颖的情况。该奖项将使AI代理在新的情况下进行理解并解释他们的决定。该项目将重点关注故事的两个关键方面：了解情况并判断上下文中行动的充分性。该项目将测试AI代理人完成叙事的能力，并就可解释的自然语言推断的任务提供常识性解释。可以期望对AI代理的解释可以提高公众对AI技术的信任。在社会AI助理中，旨在增加自闭症谱系障碍儿童的参与以及老年人对阿尔茨海默氏症的痴呆症的儿童的参与，具有常识性的强大和可解释的AI也存在着严重缺失。调查人员将设计一套新的讲座和一个关于“常识的AI助手”主题的完整课程，这些主题将在USC和国际上教授。跨学科研究将通过夏季国际职位进行准备，并参与南加州大学（USC）知识驱动的跨学科数据科学中心和本科生计划的NSF研究经验。研究人员将与USC的科学与工程学工程多样性和妇女中心合作，以招募历史上代表性不足的小组的成员进行该项目的研究。研究人员将与USC的K-12 STEM中心合作，与历史上代表性不足的小组的K-12学生与K-12学生进行合作。该奖项将通过将神经语言建模的进步与基于逻辑公理和通讯知识的高级解释结合在一起，从而在AI代理的发展中范式转变。最先进的技术不足以实现这一目标：神经方法不能直接从叙述中推断事件与代理人的动机和目标之间的因果关系，而单独常识的公理和知识资源无法处理人类语言的上下文变化。研究人员团队将建立使用常识来解释其推理的AI代理。为此，研究人员将利用常识性知识和关于代理心理学和事件偶然性的公理，以丰富故事语料库。丰富的数据将用于预先培训神经符号剂，以完成开放世界的叙述并通过常识解释证明其完成。研究人员将衡量代表性技术，公理理论和知识维度对理解情况和行动的叙述的影响。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来审查标准的评估，被认为是宝贵的支持。