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.

该奖项全部或部分根据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，提供有关现实世界叙事的常识性解释。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定。该项目将侧重于故事的两个关键方面：理解情况和判断背景中的行动是否充分。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解释。人工智能代理的可解释性有望提高公众对人工智能技术的信任。具有常识的强大且可解释的AI在社交AI助手中也严重缺失，这些助手旨在增加自闭症谱系障碍儿童和阿尔茨海默氏痴呆症老年人的参与。研究人员将设计一套新的讲座和一门关于“具有常识的人工智能助理”的完整课程，这些课程将在南加州大学和国际上教授。跨学科研究将通过暑期实习，并在现有的南加州（USC）知识驱动的跨学科数据科学中心和NSF研究经验的本科生计划参与促进。研究人员将与南加州大学的工程多样性和妇女在科学和工程中心合作，以招募历史上代表性不足的群体的成员进行本项目的研究。研究者将与南加州大学的K-12 STEM中心合作，吸引来自历史上代表性不足的群体的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公理和常识知识的高级解释相结合，在人工智能代理的开发中创造范式转变。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代理人的动机和目标之间的因果关系，而常识公理和知识资源本身无法处理人类语言中的上下文变化。研究人员团队将构建使用常识来解释推理的人工智能代理。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丰富故事语料库。丰富的数据将用于预训练神经符号代理，以完成开放世界的叙述，并用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有关情况和行动的叙述的影响。该奖项反映了NSF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。