ITR - (EVS+NHS) - (dmc + int): Knowledge Infusion

ITR - (EVS NHS) - (dmc int)：知识注入

• 批准号: 0427129
• 负责人: Leslie Valiant
• 金额: $ 90.8万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427129&HistoricalAwards=false
• 关键词: ITR; EVS+NHS; dmc; int; Knowledge

The goal of the proposed research is to extend the reach of machine learning technology so that it can enable computers to perform a broader span of tasks than currently. In particular the goal is to enable machines to extract knowledge from data into a form such that robust reasoning can be done on it. Currently representations on which reasoning can be done on a large scale are typically programmed and the results suffer from brittleness - they do not behave well in unforeseen situations. In the proposed approach, which is called knowledge infusion, the goal is to acquire the rules on which reasoning will be done by a combination of programming and learning, and to have a continuous process of learning and checking against an environment to ensure that the rules are reliable. The goal is to handle unaxiomatized or commonsense knowledge, which encompasses the bulk of knowledge that humans handle everyday, as embodied in speech or text, replete as these often are with inconsistencies, ambiguities and errors. This can be distinguished from knowledge that is known to be axiomatizable, such as most knowledge of a mathematical nature. Axiomatized knowledge, in general, can be easily programmed, and computers can usually fully exploit such knowledge up to any inherent computational complexity limitations of the problem at hand. The most central aims of the research are the development of algorithms that realize knowledge infusion and are computationally efficient and effective even for very large datasets. Also central is the identification of what the fundamental limits of the phenomenon are. The techniques used will be from theoretical computer science, and experimentation on large datasets will be carried out as needed. The goal is to be able to infuse into machines commonsense knowledge about the world on a large scale and in a way such that the machines will be able to reason with it with a controlled level of robustness. Success in this endeavor can be expected to have applications in almost all areas of computing that involve either human interaction with a computer, or computation on data that was generated by or has reference to humans. Hence there are numerous connections with the national priority areas EVS and NHS, and with the technical focus areas int and dmc. Broader Impact: If successful the results of the research will help enhance the effectiveness of computers to handle commonsense or unaxiomatized information about the world. This would extend the usefulness of computers to new areas and contribute to prosperity (EVS). It would also enable large datasets to be analyzed automatically with greater functionality than hitherto (NHS).

拟议研究的目标是扩展机器学习技术的范围，使计算机能够执行比目前更广泛的任务。具体来说，目标是使机器能够从数据中提取知识，并将其转化为一种可以对其进行稳健推理的形式。目前，可以进行大规模推理的表示通常是程序化的，其结果存在脆弱性——它们在不可预见的情况下表现不佳。在这种被称为知识注入的方法中，目标是通过编程和学习的结合来获得推理所依据的规则，并有一个连续的学习过程和对环境的检查，以确保规则是可靠的。目标是处理非公理化的或常识性的知识，这包括人类日常处理的大部分知识，体现在语音或文本中，因为这些通常充满了不一致、含糊和错误。这可以与已知可公理化的知识区别开来，例如大多数数学性质的知识。一般来说，公理化的知识可以很容易地编程，并且计算机通常可以充分利用这些知识，直到手头问题的任何固有计算复杂性限制。该研究的最核心目标是开发实现知识注入的算法，即使对于非常大的数据集，计算效率也很高。同样重要的是确定这种现象的基本限制是什么。所使用的技术将来自理论计算机科学，并根据需要在大型数据集上进行实验。我们的目标是能够将关于世界的常识大规模地注入机器，并以某种方式使机器能够在可控的鲁棒性水平下进行推理。这一努力的成功可以预期在几乎所有计算领域都有应用，这些领域要么涉及人类与计算机的交互，要么涉及对由人类生成或与人类有关的数据的计算。因此，与国家优先领域EVS和NHS以及技术重点领域int和dmc有许多联系。更广泛的影响：如果成功，研究结果将有助于提高计算机处理关于世界的常识或非公理信息的有效性。这将把计算机的用途扩展到新的领域，并促进繁荣（EVS）。它还将使大型数据集能够自动分析，其功能比迄今为止（NHS）更强大。