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The idea of reducing planning problems into satisfiability (SAT) was first introduced in ( Kautz, McAllester, & Selman, 1996). SAT solvers are used today for a variety of applications from planning to program verification and are considered among the fastest solutions for applied np-hard problems ( Harmelin, Lifschitz, & Porter
Will this course require programming? – We will work with several off-the-shelf representation and reasoning tools. – We will not be writing any new tools from scratch. – The focus will be on applying representation techniques to real world knowledge and using existing tools to reason with that knowledge.
The field of Artificial intelligence: – The design and study of computer systems that behave intelligently. • AI programs: – Go beyond numerical computations and manipulations. – Focus on problems that require reasoning (intelligence). – and often a great deal of knowledge about the world. • Success in solving the problems
1.1 Knowledge Representation. Artificial Intelligence (AI). A field of computer science and engineering concerned with the computational understanding of what is commonly called intelligent behavior, and with the creation of artifacts that exhibit such behavior. Page 5
What is Knowledge? 2. What is a Knowledge Representation? 3. Requirements of a Knowledge Representation. 4. Practical Aspects of Good Representations. 5. Knowledge Representation in Natural Language. 6. Databases as a Knowledge Representation. 7. Frame Based Systems and Semantic Networks. 8. First Order
Knowledge Representation. Arthur B. Markman. University of Texas at Austin. E LAWRENCE ERLBAUM ASSOCIATES, PUBLISHERS. 1999 Mahwah, New Jersey London
Artificial Intelligence Methods. Marc Erich Latoschik. AI & VR Lab. Artificial Intelligence Group. University of Bielefeld. Knowledge Representation. *parts from (Russel & Norvig, 2004) Chapter 10
Slides. 1. Knowledge Representation. Introduction – KR model, typology, relationship, framework, mapping, forward & backward representation, system requirements; KR schemes - relational, inheritable, inferential, declarative, procedural; KR issues - attributes, relationship, granularity. 03-26. 2. KR Using Predicate Logic.
Handbook of Knowledge Representation. Edited by. Frank van Harmelen. Vrije Universiteit Amsterdam. The Netherlands. Vladimir Lifschitz. University of Texas at Austin. USA. Bruce Porter. University of Texas at Austin. USA. AMSTERDAM–BOSTON–HEIDELBERG–LONDON–NEW YORK–OXFORD. PARIS–SAN
Abstract. Although knowledge representation is one of the central and in some ways most familiar concepts in AI, the most fundamental question about it|What is it?|has rarely been answered directly. Numerous papers have lobbied for one or another vari- ety of representation, other papers have argued for various

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February 2018