'Monotonic reasoning' diaporamas de présentation

Knowledge representation 22c:145 Artificial Intelligence The university of iowa

Knowledge representation 22c:145 Artificial Intelligence The university of iowa. Contrary to the beliefs of early workers in AI, experience has shown that Intelligent Systems cannot achieve anything useful unless they contain a large amount of real-world - probably domain-specific - knowledge.

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CSNB234 ARTIFICIAL INTELLIGENCE

CSNB234 ARTIFICIAL INTELLIGENCE. Chapter 4 Refutation and Resolution Proof (Part I). (Chapter 13, pp. 547-574, Textbook). Instructor: Alicia Tang Y. C. Proof by Refutation & Resolution. Resolution is a simple iterative process or procedure for deducing conclusions .

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Knowledge Representation and Reasoning

Knowledge Representation and Reasoning. Chapter 12. Some material adopted from notes by Andreas Geyer-Schulz and Chuck Dyer. Overview. Approaches to knowledge representation Deductive/logical methods Forward-chaining production rule systems Semantic networks Frame-based systems

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Overview of Non-Monotonic Reasoning

Overview of Non-Monotonic Reasoning. Jacques Robin. Outline. Monotonic vs. Non-Monotonic Reasoning (NMR) Epistemic vs. ontological non-monotonicity Epistemic non-monotonic automated reasoning tasks Default Reasoning (DR) Negation As Failure (NAF) in General Logic Programming (GLP)

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Knowledge Representation and Reasoning

Master of Science in Artificial Intelligence, 2010-2012. Knowledge Representation and Reasoning. University "Politehnica" of Bucharest Department of Computer Science Fall 2010 Adina Magda Florea http://turing.cs.pub.ro/krr_10 curs.cs.pub.ro. Lecture 4. ASSUMPTION BASED REASONING

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Knowledge Representation and Reasoning

Master of Science in Artificial Intelligence, 2011-2013. Knowledge Representation and Reasoning. University "Politehnica" of Bucharest Department of Computer Science Fall 2011 Adina Magda Florea http://turing.cs.pub.ro/krr_11 curs.cs.pub.ro. Lecture 3. ASSUMPTION BASED REASONING

322 views • 21 slides

Knowledge Representation and Reasoning

Master of Science in Artificial Intelligence, 2011-2013. Knowledge Representation and Reasoning. University "Politehnica" of Bucharest Department of Computer Science Fall 2011 Adina Magda Florea http://turing.cs.pub.ro/krr_11 curs.cs.pub.ro. Lecture 3. ASSUMPTION BASED REASONING

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CS344: Introduction to Artificial Intelligence (associated lab: CS386)

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Commonsense reasoning

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Negation by Failure

Negation by Failure. t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/. Motivation. p(X) :- q(X). q(a). ?-q(a). true ?-p(a). true ?-q(b). false ?-p(b). false

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Symbolic Reasoning under uncertainty

Story so far We have described techniques for reasoning with a complete, consistent and unchanging model of the world. But in many problem domains, it is not possible to create such models. So here we are going to explore techniques for solving problems with incomplete and uncertain models.

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Artificial Intelligence

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Towards Parallel Nonmonotonic Reasoning with Billions of Facts

4 IBM Research, Ireland. 1 University of Crete Department of Computer Science. Motivation for this Paper.

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