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网络规模推理和LarKC项目 (回顾和进展)

Web Scale Reasoning and the LarKC Project (Review and Progress)
课程网址: http://videolectures.net/coinactivess2010_witbrock_lkc/  
主讲教师: Michael Witbrock
开课单位: 沃森研究中心
开课时间: 2010-11-24
课程语种: 英语
中文简介:
“扩展自动推理可以自下而上,但可以对大量数据进行极其简单的推理,或者自上而下,但利用强大的规则和复杂的推理来扩大数据可以解决的问题范围。第一部分描述了Witbrock博士是技术总监的欧盟资助的LarKC项目。在InLarKC中,支持对RDF进行简单的语义Web推理,但是它的扩展是并行性的,应用了认知的算法,流推理。用新颖的,甚至是非逻辑的推理机制进行实验的能力。对于第二种方法,描述了Cyc系统。由美国和斯洛文尼亚的Cycorpin生成的Cyc支持对大规则集和高度异构知识库的一阶和更高推理。它还具有高度发展的NL查询和知识捕获机制。通过结合Cyc的“深度和多样化”方法使用LarKC的“浅而广泛”的方法,有可能将自动推理扩展到真正的AI“。
课程简介: "Scaling automated reasoning can be approached from the bottom up, but speeding up extremely simple reasoning over large numbers of data, or from the top down, but harnessing powerful rules and complex inference to broaden the range of problems that data can address. In this talk, both approaches are discussed. For the first, the EU-Funded LarKC project, of which Dr Witbrock is technical director, is described. In LarKC, simple, semantic web inference over RDF is supported, but its reach is extended with parallelism, application of cognitively-inspired algorithms, stream reasoning, and the ability to experiment with novel, even non-logical, inference mechanisms. For the second approach, the Cyc system is described. Cyc, produced by Cycorp in the US and Slovenia, supports first order and higher inference over very large rule sets and highly heterogeneous knowledge bases. It also has a highly developed mechanism for NL query and knowledge capture. By combining the "deep and diverse" approach of Cyc with the "shallow but broad" approach of LarKC, it may be possible to scale automated reasoning towards genuine AI".
关 键 词: AI; 认知的算法; 自动推理
课程来源: 视频讲座网
最后编审: 2019-03-05:lxf
阅读次数: 77