Reasoning And Modeling Support For Logic Based Knowledge Representation
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Reasoning And Modeling Support For Logic Based Knowledge Representation
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Author : Sebastian Rudolph
language : en
Publisher:
Release Date : 2011
Reasoning And Modeling Support For Logic Based Knowledge Representation written by Sebastian Rudolph and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Handbook Of Knowledge Representation
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Author : Frank van Harmelen
language : en
Publisher: Elsevier
Release Date : 2008-01-08
Handbook Of Knowledge Representation written by Frank van Harmelen and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-08 with Computers categories.
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter * Handle qualitative and uncertain information * Improve computational tractability to solve your problems easily
Part Whole Reasoning In An Object Centered Framework
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Author : Patrick Lambrix
language : en
Publisher: Springer
Release Date : 2003-06-29
Part Whole Reasoning In An Object Centered Framework written by Patrick Lambrix and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-29 with Computers categories.
In this book, the author develops an object-centered framework with specialized support of the part-of relation based on description logics. These logics are a family of object-centered knowledge representation languages tailored for describing knowledge about concepts and is-a hierarchies of these concepts. In addition to the representation and reasoning facilities provided by description logics for is-a, representation and reasoning facilities are introduced for part-of. Finally, the feasibility and the usefulness of the approach is demonstrated by applying the framework to various areas including domain modeling, agent-oriented scenarios, document management and retrieval, and composite concept learning.
The Logic Of Knowledge Bases
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Author : Hector Levesque
language : en
Publisher:
Release Date : 2023-01-02
The Logic Of Knowledge Bases written by Hector Levesque and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-02 with categories.
The idea of a knowledge base lies at the heart of symbolic or "good old-fashioned" artificial intelligence (GOFAI). A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge, its knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know, and expected to infer the rest. This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way, the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a rigorous mathematical model of knowledge that is general and expressive, yet more workable in practice than previous models. The first edition of the book appeared in the year 2000, and since then its model of knowledge has been applied and extended in a number of ways. This second edition incorporates a number of new results about the logic of knowledge bases, including default reasoning, reasoning about action and change, and tractable reasoning. Hector Levesque is Professor Emeritus in the Department of Computer Science, University of Toronto. Gerhard Lakemeyer is Professor and Chair of the Department of Computer Science, RWTH Aachen University, and Professor (status only) in the Department of Computer Science, University of Toronto.
Logic Based Artificial Intelligence
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Author : Jack Minker
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Logic Based Artificial Intelligence written by Jack Minker and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
The use of mathematical logic as a formalism for artificial intelligence was recognized by John McCarthy in 1959 in his paper on Programs with Common Sense. In a series of papers in the 1960's he expanded upon these ideas and continues to do so to this date. It is now 41 years since the idea of using a formal mechanism for AI arose. It is therefore appropriate to consider some of the research, applications and implementations that have resulted from this idea. In early 1995 John McCarthy suggested to me that we have a workshop on Logic-Based Artificial Intelligence (LBAI). In June 1999, the Workshop on Logic-Based Artificial Intelligence was held as a consequence of McCarthy's suggestion. The workshop came about with the support of Ephraim Glinert of the National Science Foundation (IIS-9S2013S), the American Association for Artificial Intelligence who provided support for graduate students to attend, and Joseph JaJa, Director of the University of Maryland Institute for Advanced Computer Studies who provided both manpower and financial support, and the Department of Computer Science. We are grateful for their support. This book consists of refereed papers based on presentations made at the Workshop. Not all of the Workshop participants were able to contribute papers for the book. The common theme of papers at the workshop and in this book is the use of logic as a formalism to solve problems in AI.
Knowledge Representation And Inductive Reasoning Using Conditional Logic And Sets Of Ranking Functions
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Author : S. Kutsch
language : en
Publisher: IOS Press
Release Date : 2021-02-09
Knowledge Representation And Inductive Reasoning Using Conditional Logic And Sets Of Ranking Functions written by S. Kutsch and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.
A core problem in Artificial Intelligence is the modeling of human reasoning. Classic-logical approaches are too rigid for this task, as deductive inference yielding logically correct results is not appropriate in situations where conclusions must be drawn based on the incomplete or uncertain knowledge present in virtually all real world scenarios. Since there are no mathematically precise and generally accepted definitions for the notions of plausible or rational, the question of what a knowledge base consisting of uncertain rules entails has long been an issue in the area of knowledge representation and reasoning. Different nonmonotonic logics and various semantic frameworks and axiom systems have been developed to address this question. The main theme of this book, Knowledge Representation and Inductive Reasoning using Conditional Logic and Sets of Ranking Functions, is inductive reasoning from conditional knowledge bases. Using ordinal conditional functions as ranking models for conditional knowledge bases, the author studies inferences induced by individual ranking models as well as by sets of ranking models. He elaborates in detail the interrelationships among the resulting inference relations and shows their formal properties with respect to established inference axioms. Based on the introduction of a novel classification scheme for conditionals, he also addresses the question of how to realize and implement the entailment relations obtained. In this work, “Steven Kutsch convincingly presents his ideas, provides illustrating examples for them, rigorously defines the introduced concepts, formally proves all technical results, and fully implements every newly introduced inference method in an advanced Java library (...). He significantly advances the state of the art in this field.” – Prof. Dr. Christoph Beierle of the FernUniversität in Hagen
Knowledge Reasoning
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-04
Knowledge Reasoning written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-04 with Computers categories.
What Is Knowledge Reasoning Knowledge representation and reasoning is a subfield of artificial intelligence (AI) that is devoted to the challenge of describing information about the world in a format that a computer system can use to solve complex problems, such as diagnosing a medical condition or having a conversation in a natural language. Examples of complex problems that can be solved by knowledge representation and reasoning include diagnosing a medical condition and having a conversation in a natural language. In order to construct formalisms that will make it easier to design and build complicated systems, knowledge representation combines discoveries from the field of psychology regarding how humans solve issues and represent knowledge. The application of rules and the interactions between sets and subsets are two examples of the forms of reasoning that can be automated with the help of knowledge representation and reasoning, which also incorporates results from logic. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Knowledge representation and reasoning Chapter 2: Knowledge management Chapter 3: Semantic technology Chapter 4: Knowledge graph Chapter 5: Logico-linguistic modeling Chapter 6: Conceptual graph Chapter 7: Commonsense knowledge (artificial intelligence) Chapter 8: Ontology engineering Chapter 9: Knowledge-based systems Chapter 10: Functional completeness (II) Answering the public top questions about knowledge reasoning. (III) Real world examples for the usage of knowledge reasoning in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of knowledge reasoning' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of knowledge reasoning.
Automating Business Modelling
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Author : Yun-Heh Chen-Burger
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-30
Automating Business Modelling written by Yun-Heh Chen-Burger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-30 with Business & Economics categories.
Enhances the use of enterprise models as an effective communication medium between business and technical personnel. Details the blue-print of the to-be developed business system.
Machine Learning Meta Reasoning And Logics
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Author : Pavel B. Brazdil
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Machine Learning Meta Reasoning And Logics written by Pavel B. Brazdil and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
This book contains a selection of papers presented at the International Workshop Machine Learning, Meta-Reasoning and Logics held in Hotel de Mar in Sesimbra, Portugal, 15-17 February 1988. All the papers were edited afterwards. The Workshop encompassed several fields of Artificial Intelligence: Machine Learning, Belief Revision, Meta-Reasoning and Logics. The objective of this Workshop was not only to address the common issues in these areas, but also to examine how to elaborate cognitive architectures for systems capable of learning from experience, revising their beliefs and reasoning about what they know. Acknowledgements The editing of this book has been supported by COST-13 Project Machine Learning and Knowledge Acquisition funded by the Commission o/the European Communities which has covered a substantial part of the costs. Other sponsors who have supported this work were Junta Nacional de lnvestiga~ao Cientlfica (JNICT), lnstituto Nacional de lnvestiga~ao Cientlfica (INIC), Funda~ao Calouste Gulbenkian. I wish to express my gratitude to all these institutions. Finally my special thanks to Paula Pereira and AnaN ogueira for their help in preparing this volume. This work included retyping all the texts and preparing the camera-ready copy. Introduction 1 1. Meta-Reasoning and Machine Learning The first chapter is concerned with the role meta-reasoning plays in intelligent systems capable of learning. As we can see from the papers that appear in this chapter, there are basically two different schools of thought.
Knowledge Representation And Reasoning
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Author : Ronald Brachman
language : en
Publisher: Elsevier
Release Date : 2004-06-17
Knowledge Representation And Reasoning written by Ronald Brachman and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-17 with Computers categories.
Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs. Authors are well-recognized experts in the field who have applied the techniques to real-world problems Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems Offers the first true synthesis of the field in over a decade