EAPSI: Learning Semantic Decomposition in Support of Commonsense Reasoning

EAPSI：学习语义分解以支持常识推理

• 批准号: 1713952
• 负责人: William Hancock
• 金额: $ 0.54万
• 依托单位: Hancock William
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713952&HistoricalAwards=false
• 关键词: EAPSI; Learning; Semantic; Decomposition; Support

Commonsense reasoning is the ability for humans to use everyday facts about the world to support planning. To create a story about a bank robbery, one must have knowledge of what a bank is, why one would wish to rob it, etc. Artificial intelligence (AI) agents can learn a lot about the world by reading existing text sources, such as Wikipedia or narrative. However, the information learned so far from these sources tends to be explicit, such as, person A was born at location B. This project will investigate methods that can be used to learn implicit knowledge from text such as spatial or temporal properties of concepts. This knowledge can then support many different current challenges in AI, such as narrative generation. This project will be conducted at Toyota Technological University under the mentorship of Dr. Makoto Miwa. The collaboration provides access to unique data that will enable new insights into how humans process everyday concepts.Current work on information extraction tends to learn propositions that are explicitly encoded in text. Implicit information is equally or more important in giving cognitive agents the ability to reason about the world. Under the supervision of Dr. Miwa, I will apply machine learning and analogical techniques to the task of associative concept learning. I will specifically look at low level qualia related attributes including temporality, space, sentiment, aesthetics, etc. I will use various corpora in support of this task. Dr. Miwa, along with his research group at Toyota Technological University, are leaders in information extraction techniques. Subsequently, Dr. Miwa's insight and expertise will be an invaluable aid in developing techniques for machine learning implicit knowledge.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.

常识推理是人类利用有关世界的日常事实来支持计划的能力。要创建一个有关银行抢劫的故事，必须了解银行是什么，为什么要抢劫它等。人工智能（AI）代理人可以通过阅读现有的文本来源，例如维基百科或叙事。但是，从这些来源那里学到的信息往往是显式的，例如，A A出生在位置B。该项目将调查可用于从文本中学习隐式知识的方法，例如概念的空间或时间属性。然后，这些知识可以支持AI中许多不同的当前挑战，例如叙事产生。该项目将在Makoto Miwa博士的指导下在丰田技术大学进行。该协作提供了对独特数据的访问，这些数据将使人们对人类如何处理日常概念进行新的见解。关于信息提取的现行工作倾向于学习在文本中明确编码的命题。隐式信息在赋予认知代理人推理世界的能力方面同样重要。在Miwa博士的监督下，我将把机器学习和类比技术应用于关联概念学习的任务。我将专门研究与Qualia相关的属性，包括时间性，空间，情感，美学等。我将使用各种语料库来支持此任务。 Miwa博士以及他在丰田技术大学的研究小组是信息提取技术的领导者。随后，Miwa博士的洞察力和专业知识将是为了开发机器学习隐式知识的技术的宝贵援助。根据东亚和太平洋夏季学院计划，该奖项支持美国研究生的夏季研究，并由NSF和NSF和NSF和NSF和日本促进科学学会。