That facilitates differentiating relationships

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that facilitates differentiating relationships between objects and properties of objects. Sometimes called a “slot-and-filler” representation CS 4633/6633 Artificial Intelligence Figure 10.7 from Russell and Norvig page 318 CS 4633/6633 Artificial Intelligence Multiple inheritance Animate Cartoon character subset Penquin Opus Speech member vocalization subset Squawks member vocalization CS 4633/6633 Artificial Intelligence CYC (from enCYClopedia) Project Began at MCC (Microelectronics and Computer Technology Comporation) in Austin, TX, in 1984, as a ten-year project with a $35 million grant Since 1995 has been continued by a private company, CYCORP A massive knowledge base and inference engine designed to overcome the limitations of expert system technology by formalizing common sense knowledge CS 4633/6633 Artificial Intelligence CycL CYC originally used a frame-based system to represent knowledge, but has since developed its own knowledge representation language, CycL, which is an extension of first-order logic All the knowledge in CYC is represented declaratively, as facts and rules CYC presently has close to a million facts and rules from which its inference engine can derive new conclusions using deductive reasoning The present estimate (which keeps being revised upwards) is that it needs ten to twenty million facts and rules to have common sense CS 4633/6633 Artificial Intelligence Examples of common-sense knowledge “Cars in motion generally have a driver” “Police in most countries are armed” “If you drop a glass, it will break”
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CS 4633/6633 Artificial Intelligence Natural language interface Development of a natural language interface for CYC is ongoing The goal is for CYC to learn by reading books and articles, or by having people tell it things in English Current natural language interface is useful but very primitive (this is a hard problem) CS 4633/6633 Artificial Intelligence Applications of CYC Although CYC is far from having common sense, the techniques developed in the course of this project for knowledge representation and inference have a number of applications, including: Heterogenous database browsing and integration captioned image retrieval natural language processing
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