Neural Networks For Knowledge Representation And Inference

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Neural Networks for Knowledge Representation and Inference

Author : Daniel S. Levine
Publisher : Psychology Press
ISBN 13 : 1134771614
Total Pages : 526 pages
Book Rating : 4.1/5 (347 download)

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Book Synopsis Neural Networks for Knowledge Representation and Inference by : Daniel S. Levine

Download or read book Neural Networks for Knowledge Representation and Inference written by Daniel S. Levine and published by Psychology Press. This book was released on 2013-04-15 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

Neural Networks for Knowledge Representation and Inference

Author : Daniel S. Levine
Publisher : Psychology Press
ISBN 13 : 1134771541
Total Pages : 523 pages
Book Rating : 4.1/5 (347 download)

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Book Synopsis Neural Networks for Knowledge Representation and Inference by : Daniel S. Levine

Download or read book Neural Networks for Knowledge Representation and Inference written by Daniel S. Levine and published by Psychology Press. This book was released on 2013-04-15 with total page 523 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges

Author : I. Tiddi
Publisher : IOS Press
ISBN 13 : 1643680811
Total Pages : 314 pages
Book Rating : 4.6/5 (436 download)

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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.

Knowledge-based Neurocomputing

Author : Ian Cloete
Publisher : MIT Press
ISBN 13 : 9780262032742
Total Pages : 512 pages
Book Rating : 4.0/5 (327 download)

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Book Synopsis Knowledge-based Neurocomputing by : Ian Cloete

Download or read book Knowledge-based Neurocomputing written by Ian Cloete and published by MIT Press. This book was released on 2000 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Looking at ways to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.Neurocomputing methods are loosely based on a model of the brain as a network of simple interconnected processing elements corresponding to neurons. These methods derive their power from the collective processing of artificial neurons, the chief advantage being that such systems can learn and adapt to a changing environment. In knowledge-based neurocomputing, the emphasis is on the use and representation of knowledge about an application. Explicit modeling of the knowledge represented by such a system remains a major research topic. The reason is that humans find it difficult to interpret the numeric representation of a neural network.The key assumption of knowledge-based neurocomputing is that knowledge is obtainable from, or can be represented by, a neurocomputing system in a form that humans can understand. That is, the knowledge embedded in the neurocomputing system can also be represented in a symbolic or well-structured form, such as Boolean functions, automata, rules, or other familiar ways. The focus of knowledge-based computing is on methods to encode prior knowledge and to extract, refine, and revise knowledge within a neurocomputing system.ContributorsC. Aldrich, J. Cervenka, I. Cloete, R.A. Cozzio, R. Drossu, J. Fletcher, C.L. Giles, F.S. Gouws, M. Hilario, M. Ishikawa, A. Lozowski, Z. Obradovic, C.W. Omlin, M. Riedmiller, P. Romero, G.P.J. Schmitz, J. Sima, A. Sperduti, M. Spott, J. Weisbrod, J.M. Zurada

Knowledge Representation and Reasoning with Deep Neural Networks

Author : Arvind Ramanathan Neelakantan
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Knowledge Representation and Reasoning with Deep Neural Networks by : Arvind Ramanathan Neelakantan

Download or read book Knowledge Representation and Reasoning with Deep Neural Networks written by Arvind Ramanathan Neelakantan and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve state-of-the-art reasoning on a large structured knowledge graph. Next, we develop Neural Programmer, a neural network augmented with discrete operations that can be learned to induce latent programs with backpropagation. We apply Neural Programmer to induce short programs on two datasets: a synthetic dataset requiring arithmetic and logic reasoning, and a natural language question answering dataset that requires reasoning on semi-structured Wikipedia tables. We present what is to our awareness the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. Unlike previous learning approaches to program induction, the model does not require domain-specific grammars, rules, or annotations. Finally, we discuss methods to scale Neural Programmer training to large databases.

Artificial Intelligence and Knowledge Processing

Author : Hemachandran K
Publisher : CRC Press
ISBN 13 : 1000934624
Total Pages : 372 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Artificial Intelligence and Knowledge Processing by : Hemachandran K

Download or read book Artificial Intelligence and Knowledge Processing written by Hemachandran K and published by CRC Press. This book was released on 2023-09-06 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.

Neural-Symbolic Cognitive Reasoning

Author : Artur S. D'Avila Garcez
Publisher : Springer Science & Business Media
ISBN 13 : 3540732454
Total Pages : 200 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Neural-Symbolic Cognitive Reasoning by : Artur S. D'Avila Garcez

Download or read book Neural-Symbolic Cognitive Reasoning written by Artur S. D'Avila Garcez and published by Springer Science & Business Media. This book was released on 2009 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Author : Nikola K. Kasabov
Publisher : Marcel Alencar
ISBN 13 : 0262112124
Total Pages : 581 pages
Book Rating : 4.2/5 (621 download)

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Book Synopsis Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering by : Nikola K. Kasabov

Download or read book Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering written by Nikola K. Kasabov and published by Marcel Alencar. This book was released on 1996 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.

Neural Architectures for Database Query Processing, Syntax Analysis, Knowledge Representation, and Inference

Author : Chun-Hsien Chen
Publisher :
ISBN 13 :
Total Pages : 318 pages
Book Rating : 4.:/5 (399 download)

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Book Synopsis Neural Architectures for Database Query Processing, Syntax Analysis, Knowledge Representation, and Inference by : Chun-Hsien Chen

Download or read book Neural Architectures for Database Query Processing, Syntax Analysis, Knowledge Representation, and Inference written by Chun-Hsien Chen and published by . This book was released on 1997 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANN), due to their inherent parallelism, potential for fault tolerance, and adaptation through learning, offer an attractive computational paradigm for a variety of applications in computer science and engineering, artificial intelligence, robotics, and cognitive modeling. Despite the success in the application of ANN to a broad range of numeric tasks in pattern classification, control, function approximation, and system identification, the integration of ANN and symbolic computing is only beginning to be explored. This dissertation explores to integrate ANN and some essential symbolic computations for content-based associative symbolic processing.

Knowledge-Based Systems

Author : Rajendra Akerkar
Publisher : Jones & Bartlett Publishers
ISBN 13 : 1449662706
Total Pages : 375 pages
Book Rating : 4.4/5 (496 download)

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Book Synopsis Knowledge-Based Systems by : Rajendra Akerkar

Download or read book Knowledge-Based Systems written by Rajendra Akerkar and published by Jones & Bartlett Publishers. This book was released on 2009-08-25 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.

Knowledge Representation with Artificial Neural Networks

Author : Andrew Benjamin Smith
Publisher :
ISBN 13 :
Total Pages : 538 pages
Book Rating : 4.:/5 (728 download)

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Book Synopsis Knowledge Representation with Artificial Neural Networks by : Andrew Benjamin Smith

Download or read book Knowledge Representation with Artificial Neural Networks written by Andrew Benjamin Smith and published by . This book was released on 1991 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Knowledge-Based Systems

Author : Rajendra Akerkar
Publisher : Jones & Bartlett Learning
ISBN 13 : 0763776475
Total Pages : 375 pages
Book Rating : 4.7/5 (637 download)

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Book Synopsis Knowledge-Based Systems by : Rajendra Akerkar

Download or read book Knowledge-Based Systems written by Rajendra Akerkar and published by Jones & Bartlett Learning. This book was released on 2010-08-30 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

Multistrategy Learning

Author : Ryszard S. Michalski
Publisher : Springer Science & Business Media
ISBN 13 : 1461532027
Total Pages : 156 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Multistrategy Learning by : Ryszard S. Michalski

Download or read book Multistrategy Learning written by Ryszard S. Michalski and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.

Artificial Intelligence and Neural Networks

Author : Vasant Honavar
Publisher :
ISBN 13 :
Total Pages : 696 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Artificial Intelligence and Neural Networks by : Vasant Honavar

Download or read book Artificial Intelligence and Neural Networks written by Vasant Honavar and published by . This book was released on 1994 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: A growing body of research focuses on how the strengths of traditional artificial intelligence and neural networks can be incorporated into systems that include the best features of both. Artificial Intelligence and Neural Networks: Steps toward Principled Integration provides readers with a critical examination of the key issues, underlying assumptions, and relevant suggestions related to the reconciliation and principled integration of artificial intelligence and neural networks into successful hybrid systems. A comprehensive introduction to the basics of symbol processing and connectionist networks, and their integration gives readers the necessary background to understand each network system. Numerous examples of the integration of artificial and neural networks for a variety of specific applications, including vision and pattern recognition, illustrate the exciting possibilities and actualities of the resultant hybrid systems. With contribution from some of the leading researchers in the field, this book offers a unique view into this evolving area. -- Back cover.

KI 2003: Advances in Artificial Intelligence

Author : Andreas Günter
Publisher : Springer
ISBN 13 : 3540394516
Total Pages : 675 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis KI 2003: Advances in Artificial Intelligence by : Andreas Günter

Download or read book KI 2003: Advances in Artificial Intelligence written by Andreas Günter and published by Springer. This book was released on 2003-09-09 with total page 675 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 26th Annual German Conference on Artificial Intelligence, KI 2003, held in Hamburg, Germany in September 2003. The 42 revised full papers presented together with 5 invited papers were carefully reviewed and selected from 90 submissions from 22 countries. The papers are organized in topical sections on logics and ontologies, cognitive modeling, reasoning methods, machine learning, neural networks, reasoning under uncertainty, planning and constraints, spatial modeling, user modeling, and agent technology.

Neural Networks for Control

Author : Wilhelmus Theodor Christiaan van Luenen
Publisher :
ISBN 13 : 9789090066455
Total Pages : 125 pages
Book Rating : 4.0/5 (664 download)

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Book Synopsis Neural Networks for Control by : Wilhelmus Theodor Christiaan van Luenen

Download or read book Neural Networks for Control written by Wilhelmus Theodor Christiaan van Luenen and published by . This book was released on 1993 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Knowledge Engineering

Author : Simon Kendal
Publisher : Springer Science & Business Media
ISBN 13 : 1846286670
Total Pages : 294 pages
Book Rating : 4.8/5 (462 download)

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Book Synopsis An Introduction to Knowledge Engineering by : Simon Kendal

Download or read book An Introduction to Knowledge Engineering written by Simon Kendal and published by Springer Science & Business Media. This book was released on 2007-08-08 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Knowledge Engineering presents a simple but detailed exp- ration of current and established work in the ?eld of knowledge-based systems and related technologies. Its treatment of the increasing variety of such systems is designed to provide the reader with a substantial grounding in such techno- gies as expert systems, neural networks, genetic algorithms, case-based reasoning systems, data mining, intelligent agents and the associated techniques and meth- ologies. The material is reinforced by the inclusion of numerous activities that provide opportunities for the reader to engage in their own research and re?ection as they progress through the book. In addition, self-assessment questions allow the student to check their own understanding of the concepts covered. The book will be suitable for both undergraduate and postgraduate students in computing science and related disciplines such as knowledge engineering, arti?cial intelligence, intelligent systems, cognitive neuroscience, robotics and cybernetics. vii Contents Foreword vii 1 An Introduction to Knowledge Engineering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Section 1: Data, Information and Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Section 2: Skills of a Knowledge Engineer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Section 3: An Introduction to Knowledge-Based Systems. . . . . . . . . . . . . . . . . 18 2 Types of Knowledge-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Section 1: Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Section 2: Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Section 3: Case-Based Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Section 4: Genetic Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Section 5: Intelligent Agents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Section 6: Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 3 Knowledge Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 4 Knowledge Representation and Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Section 1: Using Knowledge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Section 2: Logic, Rules and Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Section 3: Developing Rule-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Section 4: Semantic Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .