Tractable reasoning in incomplete first-order knowledge bases

by Yongmei Liu

Written in English
Published: Pages: 180 Downloads: 375
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Edition Notes

Statementby Yongmei Liu.
The Physical Object
Paginationx, 180 leaves.
Number of Pages180
ID Numbers
Open LibraryOL20763001M
ISBN 109780494157602

Lec06 AI Knowledge Representation - Free download as Powerpoint Presentation .ppt /.pptx), PDF File .pdf), Text File .txt) or view presentation slides online. Knowledge representation . Reasoning-Supported Interactive Revision of Knowledge Bases Nadeschda Nikitina, Sebastian Rudolph, Birte Glimm. Augmenting Tractable Fragments of Abstract Argumentation Sebastian Ordyniak, Stefan . Using Incomplete Quantitative Knowledge in Qualitative Reasoning / Benjamin Kuipers and Daniel Berleant, University of Texas at Austin. Design. Assembling a Device / Jean-Luc Dormoy, . assertional box formalism should be able to represent incomplete knowledge in a limited manner, (iv) the system should allow for extending the knowledge base incrementally (retractions are not considered) .

T. K. Satish Kumar and Stuart Russell, ``On Some Tractable Cases of Logical Filtering.'' In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS ), . A preliminary version appears in Proceedings of the Second Conference on Theoretical Aspects of Reasoning About Knowledge, , pp. Decidability and expressiveness for first-order logics . Logic is the science of Reasoning in the sense in which 'reasoning' means giving reasons, for it shows what sort of reasons are good. Whilst Psychology explains how the mind goes forward from data to . Chitta Baral - Knowledge Representation Reasoning and Declarative Problem Solving ( Cambridge University Press).pdf.

  Abstract. This is the report of the W3C Uncertainty Reasoning for the World Wide Web Incubator Group (URW3-XG) as specified in the Deliverables section of its charter.. In this report we . Resolving conflicts in knowledge for ambient intelligence - Volume 30 Issue 5 - Martin Homola, Theodore Patkos, Giorgos Flouris, Ján Šefránek, Alexander Šimko, Jozef Frtús, Dimitra Zografistou, Martin BalážCited by: 5. The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors. When all saddle points are non-degenerate, all second . Andrew McCallum, Xuerui Wang and Natasha Mohanty, Statistical Network Analysis: Models, Issues and New Directions, Lecture Notes in Computer Science , pp. , (Book chapter), (Book .

Tractable reasoning in incomplete first-order knowledge bases by Yongmei Liu Download PDF EPUB FB2

Tractable Reasoning in First-Order Knowledge Bases with Disjunctive Information Yongmei Liu and Hector J. Levesque Department of Computer Science University of Toronto Toronto, ON, Canada. Tractable First-Order Golog with Disjunctive Knowledge Bases.

which lends itself to efficient reasoning in incomplete first-order knowledge bases. In particular,SL defines levels of belief Author: Gerhard Lakemeyer. Tractable Reasoning in First-Order Knowledge Bases with Disjunctive Information. Conference Paper (PDF Available) January with 11 Reads How we measure 'reads'.

A Completeness Result for Reasoning with Incomplete First-Order Knowledge Bases We shall therefore say that a program has com-mon sense if it automatically deduces for it-self a sufficientlywide class Cited by: Tractable First-Order Golog with Disjunctive Knowledge Bases Jens Claßen and Gerhard Lakemeyer in incomplete first-order knowledge bases.

In particular, SL reasoning is also tractable. Cited by: 4. here a framework for understanding and analyzing reasoning about knowledge that is intuitive, mathematicallywell founded, useful in practice, and widely applicable. The book is almost completely File Size: KB.

Building efficient large-scale knowledge bases (KBs) is a longstanding goal of AI. KBs need to be first-order to be sufficiently expressive, and probabilistic to handle uncertainty, but these lead to Cited by: 4. Limited reasoning in first-order knowledge bases with full introspection [ illustrates a major source of the complexity of reasoning about incomplete knowledge.

* E-mail: [email protected]   Abstract. Levesque’s proper knowledge bases (proper KBs) correspond to infinite sets of ground positive and negative facts, with the notable property that for FOL formulas in a certain normal Author: Giuseppe Giacomo, Hector Levesque.

Lakemeyer, Limited reasoning in first-order knowledge bases with introspection (in preparation). [28] G. Lakemeyer and S. Meyer, Enhancing the power of a decidable first-order reasoner, in: Proceedings Cited by: Default reasoning from conditional knowledge bases: In this paper, we fill these gaps and first draw a precise picture of the complexity of default reasoning from conditional knowledge bases: A natural Cited by: The main topic of this book is reasoning among a group of agents, as opposed to the reasoning that is done by a single agent in isolation.

The book begins with an excellent discussion of the semantics of. In order to realize a practical knowledge base system in the framework of the first order logic, we must overcome this problem. In this paper, we propose a time-bounded reasoning and Cited by: 1.

Knowledge-Based Systems Concepts, Techniques, Examples Reid G. Smith Schlumberger-Doll Research Old Quarry Road Ridgefield, CT USA Presented at the Canadian High Technology.

to standard first-order logic is an im-portant result, given that standard first-order logic is far better understood SPRING Book Reviews The Logic of Knowledge Bases A Review Enrico Motta. This work proposes a new methodology for establishing the tractability of a reasoning service that deals with expressive first-order knowledge bases.

It consists of defining a logic that is weaker than classical. learning and to provide a detailed explanation of case-based reasoning. Part 1: Introduction to Machine Learning This chapter introduces the term “machine learning” and defines what do we mean while File Size: KB.

The Internet Archive offers o, freely downloadable books and texts. There is also a collection of million modern eBooks that may be borrowed by anyone with a free account.

Borrow a Book. A Reasoning System for a First-Order Logic of Limited Belief Christoph Schwering Dealing with incomplete knowledge is one of the longstanding Reasoning in proper+ knowledge bases is Cited by: 1. Maurizio Lenzerini is a professor in Computer Science and Engineering at the Università di Roma La Sapienza, Italy, where he is currently leading a research group on Artificial Intelligence and.

Lecture Series on Artificial Intelligence by na Sarkar and Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur. For more details on NPTEL visit. The second step was a preliminary analysis on communication involving reasoning rules [].In that paper, a model for assertions and concessions regarding reasoning rules was proposed, Cited by: 2.

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans. The topics covered in the book are commonsense reasoning, knowledge representation, nonmonotonic reasoning, logic for causation and actions, planning and problem solving, cognitive robotics, logic for.

Logic and Artificial Intelligence The Role of Logic in Artificial Intelligence. Theoretical computer science developed out of logic, the theory of computation (if this is to be considered a different subject.

Differences between basic, complex and terminological facts in a Knowledge Base using First-Order Logic I've been reading the excellent book Knowledge Representation and Reasoning by Ronald. This book constitutes the refereed proceedings of the 9th International Conference on Scalable Uncertainty Management, SUMheld in Québec City, QC, Canada, in September The 25.

() Consequence-based and fixed-parameter tractable reasoning in description logics. Artificial Intelligence() Fixed-parameter algorithms for the cocoloring by:   Knowledge bases are important for AI and expert system developments.

A general way to represent knowledge bases is through logic. Work developed for extended DDBs concerning. rule mining in ontological knowledge bases with AMIE+.

The VLDB Journal, 24(6), [18] Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek. AMIE: association rule mining under incomplete evidence in ontological knowl-edge.

In Proceedings of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages – AAAI Press. ISBN:The second major of the book provides a detailed exploration of the applicability of one particular logical framework, Probabilistic Logic Networks, to real-world reasoning problems.

This part is different from .book: Proc. of the 10th Int. Conf. on the Principles of Knowledge Representation and Reasoning (KR ) - () / - - Epistemic First-Order Queries over Description .