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世界上的日常事实来支持计划的能力。为了创造一个关于银行抢劫的故事，人们必须知道银行是什么，为什么要抢劫它，等等。人工智能（AI）代理可以通过阅读现有的文本来源，如维基百科或叙述，了解很多关于世界的知识。然而，到目前为止从这些来源学到的信息往往是明确的，例如，人A出生在地点B。本项目将研究可用于从文本中学习隐含知识的方法，例如概念的空间或时间属性。然后，这些知识可以支持人工智能当前面临的许多不同挑战，例如叙事生成。该项目将在丰田工业大学进行，由Makoto Miwa博士指导。这项合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有新的见解。目前的信息提取工作倾向于学习文本中明确编码的命题。内隐信息在赋予认知主体对世界进行推理的能力方面同样重要，甚至更为重要。在Miwa博士的指导下，我将把机器学习和类比技术应用到联想概念学习的任务中。我将专门研究低水平的感受性相关属性，包括时间性，空间，情感，美学等，我将使用各种语料库来支持这项任务。Miwa博士沿着他在丰田技术大学的研究小组是信息提取技术的领导者。Miwa博士的洞察力和专业知识将成为开发机器学习隐式知识技术的宝贵帮助。该奖项是由美国国家科学基金会（NSF）和日本科学促进会（Japan Society for the Promotion of Science）共同资助的东亚和太平洋夏季研究所（East Asia and Pacific Summer Institutes）项目，旨在支持美国研究生的夏季研究。