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.

该奖项全部或部分由《2021 年美国救援计划法案》（公法 117-2）资助。该项目将了解如何创建人工智能 (AI) 代理，以提供有关现实世界叙述的常识性解释。目前的人工智能代理缺乏常识机制来解释他们对日常故事的判断，并且无法应用于新的场景。该奖项将使人工智能代理能够在新的情况下进行推理并解释他们的决策。故事：理解情境并判断上下文中行动的充分性。该项目将测试人工智能代理完成叙述的能力，并为可解释的自然语言推理任务提供常识性解释，预计人工智能代理的可解释性将提高。旨在提高自闭症谱系障碍儿童和阿尔茨海默氏痴呆症老年人参与度的社交人工智能助手也严重缺乏对人工智能技术的信任。研究人员将设计一套新的讲座。以“具有常识的人工智能助手”为主题的完整课程将在南加州大学以及国际上教授，通过暑期实习和参与现有的南加州大学 (USC) 中心，将促进跨学科研究。知识驱动的跨学科数据科学和国家科学基金会本科生研究经验项目的研究人员将与南加州大学工程多样性和科学与工程领域的女性中心合作，以招募历史上代表性不足的群体的成员来参与该项目的研究。研究人员将与南加州大学的 K-12 STEM 中心合作，吸引来自历史上代表性不足群体的 K-12 学生。该奖项将通过将神经语言建模的进步与基于逻辑的高级解释相结合，为人工智能代理的开发带来范式转变。最先进的技术不足以实现这一目标：神经方法无法直接从叙述中推断事件与主体的动机和目标之间的因果关系，而常识公理和知识。仅靠资源无法处理人类语言的上下文变化。为此，研究人员将利用有关代理心理学和事件因果关系的常识知识和公理来构建人工智能代理。丰富的故事语料库。丰富的数据将用于预训练神经符号代理来完成开放世界的叙述，并用常识性解释来证明其完成的合理性。研究人员将衡量代表性技术、公理理论和知识维度对理解的影响。关于情况和行动的叙述。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。