Ground Facts, Rules and Probabilistic Inference for Cyc
published: Oct. 8, 2007, recorded: September 2007, views: 570
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
One aspect of Cyc is a very large, logic-based knowledge base that includes, inter-alia, large amounts of background knowledge over a wide variety of domains, but it is more than that; the Cyc project is an attempt to move towards general artificial intelligence by supporting automated reasoning about a very wide variety of real-world concerns. To support that goal, Cyc also encompasses, obviously enough, and inference engine able to reason over a large, contextual, knowledge base, but it also includes components for interpreting and producing natural language, acquiring knowledge and responding to user queries, and for interfacing with other software. Applying logic to representation of general knowledge, /at scale/, and using it in the production of intelligent behaviors has been difficult enough; unfortunately it is becoming clear that doing so using traditional logics is probably not sufficient, either for satisfying a long term goal of supporting general intelligence, or even for shorter term goals, like recognizing, interpreting, and elaborating descriptions of piracy events. In this talk, I'll briefly describe what Cyc is, and has been, and how it is growing, touch on an early approach to abductive reasoning and classification in a traditional logical framework, and some difficulties with that approach, and then describe recent, very initial work training the Markov Logic networks based on ground facts and rules within the millions of axioms of the Cyc KB. Finally I'll sketch a vision for a system that truly integrates both sound, deductive reasoning, and the bounded unsoundness of probabilistic classification, induction, abduction and deduction.
Download slides: ida07_ljubljana_witbrock_michael.pptx (5.6 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !