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Semantic Networks In Artificial Intelligence
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Book Synopsis Semantic Networks in Artificial Intelligence by : Fritz W. Lehmann
Download or read book Semantic Networks in Artificial Intelligence written by Fritz W. Lehmann and published by Pergamon. This book was released on 1992 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. Semantic Networks are graphic structures used to represent concepts and knowledge in computers. Key uses include natural language understanding, information retrieval, machine vision, object-oriented analysis and dynamic control of combat aircraft. This major collection addresses every level of reader interested in the field of knowledge representation. Easy to read surveys of the main research families, most written by the founders, are followed by 25 widely varied articles on semantic networks and the conceptual structure of the world. Some extend ideas of philosopher Charles S Peirce 100 years ahead of his time. Others show connections to databases, lattice theory, semiotics, real-world ontology, graph-grammers, lexicography, relational algebras, property inheritance and semantic primitives. Hundreds of pictures show semantic networks as a visual language of thought.
Book Synopsis Principles of Semantic Networks by : John F. Sowa
Download or read book Principles of Semantic Networks written by John F. Sowa and published by Morgan Kaufmann. This book was released on 2014-07-10 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.
Book Synopsis Concise Encyclopedia of Robotics by : Gibilisco
Download or read book Concise Encyclopedia of Robotics written by Gibilisco and published by McGraw Hill Professional. This book was released on 2002-11-12 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: * The perfect way to keep up with the mountain of new terms and techniques facing today's hobbyist * A clearinghouse of information for hobbyists and students interested in robotics, artificial intelligence, and electronics * 400 definitions--all presented in concise, well-illustrated, non-mathematical style favored by amateur hobbyists * Covers the latest developments and trends with an eye towards the future
Book Synopsis Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges by : I. Tiddi
Download or read book Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges written by I. Tiddi and published by IOS Press. This book was released on 2020-05-06 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Book Synopsis Handbook of Research on Computational Intelligence Applications in Bioinformatics by : Dash, Sujata
Download or read book Handbook of Research on Computational Intelligence Applications in Bioinformatics written by Dash, Sujata and published by IGI Global. This book was released on 2016-06-20 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.
Book Synopsis Principles of Semantic Networks by : John Sowa
Download or read book Principles of Semantic Networks written by John Sowa and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Semantic Networks: Explorations in the Representation of Knowledge provides information pertinent to the theory and applications of semantic networks. This book deals with issues in knowledge representation, which discusses theoretical topics independent of particular implementations. Organized into three parts encompassing 19 chapters, this book begins with an overview of semantic network structure for representing knowledge as a pattern of interconnected nodes and arcs. This text then analyzes the concepts of subsumption and taxonomy and synthesizes a framework that integrates many previous approaches and goes beyond them to provide an account of abstract and partially defines concepts. Other chapters consider formal analyses, which treat the methods of reasoning with semantic networks and their computational complexity. This book discusses as well encoding linguistic knowledge. The final chapter deals with a formal approach to knowledge representation that builds on ideas originating outside the artificial intelligence literature in research on foundations for programming languages. This book is a valuable resource for mathematicians.
Book Synopsis Understanding Religion Through Artificial Intelligence by : Justin E. Lane
Download or read book Understanding Religion Through Artificial Intelligence written by Justin E. Lane and published by Bloomsbury Publishing. This book was released on 2021-05-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Understanding Religion through Artificial Intelligence, Justin E. Lane looks at the reasons why humans feel they are part of a religious group, despite often being removed from other group members by vast distances or multiple generations. To achieve this, Lane offers a new perspective that integrates religious studies with psychology, anthropology, and data science, as well as with research at the forefront of Artificial Intelligence (AI). After providing a critical analysis of approaches to religion and social cohesion, Lane proposes a new model for religious studies, which he calls the “Information Identity System.” This model focuses on the idea of conceptual ties: links between an individual's self-concept and the ancient beliefs of their religious group. Lane explores this idea through real-world examples, ranging from the rise in global Pentecostalism, to religious extremism and self-radicalization, to the effect of 9/11 on sermons. Lane uses this lens to show how we can understand religion and culture today, and how we can better contextualize the changes we see in the social world around us.
Book Synopsis Semantic Networks by : Lokendra Shastri
Download or read book Semantic Networks written by Lokendra Shastri and published by Pitman Publishing. This book was released on 1988 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shastri’s book describes how a high-level specification of hierarchically structured knowledge about concepts and their properties may be encoded as a massively parallel network of a simple processing elements. The evidential formalization of semantic networks leads to a principled treatment of exceptions, multiple inheritance and conflicting information during inheritance, and the best match or partial match computation during recognition. This formalization offers semantically justifiable solutions to a larger class of problems than existing formulations (e.g. default logic). The network operates without the intervention of a central controller or interpreter. The knowledge as well as mechanisms for drawing limited inferences on it are encoded within the network. It uses controlled spreading activation to solve inheritance and recognition problems in time proportional to the depth of the conceptual hierarchy independent of the total number of concepts in the conceptual structure. The number of nodes in the connectionist network is at most quadratic in the number of concepts. The book has six chapters and one appendix. After the introduction in chapter 1 semantic networks their properties and formalizations are discussed in chapter 2. Especially the significance of inheritance and recognition and the evidential approach to it is pointed out here. Chapter 3 specifies a knowledge representation language. The problems of inheritance and recognition are reformulated in this language. In chapter 4 the evidential formalization and its application to inheritance and recognition are demonstrated. Section 4.1 derives an evidence combination rule. In the following two sections this rule is compared to the DEMPSTER-SHAFER evidence combination rule (section 4.2) and to the BAYES’ rule for computing conditional probabilities. The next two sections develop solutions to evidential inheritance (section 4.4) and evidential recognition (section 4.5) together with constraints for a conceptual structure. The connectionist realization of the memory network is developed in chapter 5. First the need for parallelism is discussed (section 5.1), then the connectionist model (section 5.2) and other massively parallel models of semantic memory (section 5.3) are reviewed. The connectionist encoding of the high-level specification is described in section 5.4 together with the connectivity and computational characteristics of node types. This is followed by examples of network encoding (section 5.5) and the elaboration of some implementation details (section 5.6). In section 5.7 and appendix A there is a proof that the proposed network solves the inheritance and recognition problem in accordance with the evidential formulation and in time proportional to the depth of the conceptual hierarchy. Section 5.8 describes the simulation of the proposed system on a conventional computer together with simulation runs of test examples often cited as being problematic. The book ends with a general discussion (chapter 6).
Book Synopsis Associative Networks by : Nicholas V. Findler
Download or read book Associative Networks written by Nicholas V. Findler and published by Academic Press. This book was released on 2014-05-10 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.
Book Synopsis Statistical Machine Learning by : Richard Golden
Download or read book Statistical Machine Learning written by Richard Golden and published by CRC Press. This book was released on 2020-06-24 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.
Book Synopsis Artificial Intelligence XXXVII by : Max Bramer
Download or read book Artificial Intelligence XXXVII written by Max Bramer and published by Springer Nature. This book was released on 2020-12-08 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020, which was supposed to be held in Cambridge, UK, in December 2020. The conference was held virtually due to the COVID-19 pandemic. The 23 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 44 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: neural nets and knowledge management; machine learning; industrial applications; advances in applied AI; and medical and legal applications.
Book Synopsis Fundamentals of Artificial Intelligence by : K.R. Chowdhary
Download or read book Fundamentals of Artificial Intelligence written by K.R. Chowdhary and published by Springer Nature. This book was released on 2020-04-04 with total page 730 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
Book Synopsis Handbook of Knowledge Representation by : Frank van Harmelen
Download or read book Handbook of Knowledge Representation written by Frank van Harmelen and published by Elsevier. This book was released on 2008-01-08 with total page 1034 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Open Semantic Technologies for Intelligent System by : Vladimir Golenkov
Download or read book Open Semantic Technologies for Intelligent System written by Vladimir Golenkov and published by Springer Nature. This book was released on 2020-10-24 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Open Semantic Technologies for Intelligent System, OSTIS 2020, held in Minsk, Belarus, in February 2020. The 14 revised full papers and 2 short papers were carefully reviewed and selected from 62 submissions. The papers mainly focus on standardization of intelligent systems and cover wide research fields including knowledge representation and reasoning, semantic networks, natural language processing, temporal reasoning, probabilistic reasoning, multi-agent systems, intelligent agents.
Book Synopsis Knowledge Representation and the Semantics of Natural Language by : Hermann Helbig
Download or read book Knowledge Representation and the Semantics of Natural Language written by Hermann Helbig and published by Springer Science & Business Media. This book was released on 2005-12-19 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the pres- vation of cultural achievements and their transmission from one generation to the other. During the last few decades, the ?ood of digitalized information has been growing tremendously. This tendency will continue with the globali- tion of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical und- standing and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this c- text, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the g- eration of natural language expressions from formal representations. This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.
Book Synopsis Semantic Similarity from Natural Language and Ontology Analysis by : Sébastien Harispe
Download or read book Semantic Similarity from Natural Language and Ontology Analysis written by Sébastien Harispe and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments---most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning---intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesauri or ontologies. Semantic measures are widely used today to compare units of language, concepts, instances or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterization of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity toward the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.
Book Synopsis Parallel Processing for Artificial Intelligence 1 by : L.N. Kanal
Download or read book Parallel Processing for Artificial Intelligence 1 written by L.N. Kanal and published by Elsevier. This book was released on 2014-06-28 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel processing for AI problems is of great current interest because of its potential for alleviating the computational demands of AI procedures. The articles in this book consider parallel processing for problems in several areas of artificial intelligence: image processing, knowledge representation in semantic networks, production rules, mechanization of logic, constraint satisfaction, parsing of natural language, data filtering and data mining. The publication is divided into six sections. The first addresses parallel computing for processing and understanding images. The second discusses parallel processing for semantic networks, which are widely used means for representing knowledge - methods which enable efficient and flexible processing of semantic networks are expected to have high utility for building large-scale knowledge-based systems. The third section explores the automatic parallel execution of production systems, which are used extensively in building rule-based expert systems - systems containing large numbers of rules are slow to execute and can significantly benefit from automatic parallel execution. The exploitation of parallelism for the mechanization of logic is dealt with in the fourth section. While sequential control aspects pose problems for the parallelization of production systems, logic has a purely declarative interpretation which does not demand a particular evaluation strategy. In this area, therefore, very large search spaces provide significant potential for parallelism. In particular, this is true for automated theorem proving. The fifth section considers the problem of constraint satisfaction, which is a useful abstraction of a number of important problems in AI and other fields of computer science. It also discusses the technique of consistent labeling as a preprocessing step in the constraint satisfaction problem. Section VI consists of two articles, each on a different, important topic. The first discusses parallel formulation for the Tree Adjoining Grammar (TAG), which is a powerful formalism for describing natural languages. The second examines the suitability of a parallel programming paradigm called Linda, for solving problems in artificial intelligence. Each of the areas discussed in the book holds many open problems, but it is believed that parallel processing will form a key ingredient in achieving at least partial solutions. It is hoped that the contributions, sourced from experts around the world, will inspire readers to take on these challenging areas of inquiry.
