Principles of Efficient Inference

高效推理原理

• 批准号: 0120307
• 负责人: Henry Kautz
• 金额: $ 42万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0120307&HistoricalAwards=false
• 关键词: Principles; Efficient; Inference

Principles of Efficient InferenceThis is the first year funding of a three year continuing award. This project seeks to uncover fundamental principles for the construction of real-time AI systems that employ declarative knowledge representations and general reasoning engines. To achieve this goal, the PI will (a) study the "fine grained" structure of problem hardness based on notions coming out of work on phase transitions in random problem distributions; (b) develop faster complete and incomplete reasoning engines, including systems that employ decision-theoretic control of reasoning; and (c) apply and test the new algorithms to planning problems in a robotics testbed. This research will lead to the creation of useful new algorithms for solving hard combinatorial problems in areas such as knowledge-based expert systems, autonomous systems, and operations research. The results will also promote interdisciplinary work on logic and reasoning in the AI, theory, OR, robotics, and verification communities.

有效推理原则这是一个为期三年的持续奖励的第一年资助。该项目旨在揭示使用陈述性知识表示和一般推理引擎构建实时人工智能系统的基本原则。为了实现这一目标，PI将(a)基于随机问题分布中相变的概念研究问题硬度的“细粒度”结构；(b)开发更快的完整和不完整推理引擎，包括采用决策理论控制推理的系统；(c)在机器人测试平台上应用和测试新算法来规划问题。这项研究将产生有用的新算法，用于解决诸如基于知识的专家系统、自治系统和运筹学等领域的困难组合问题。研究结果还将促进人工智能、理论、OR、机器人和验证社区在逻辑和推理方面的跨学科工作。