Neural Networks For Knowledge Representation And Inference

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Neural Networks for Knowledge Representation and Inference

Daniel S. Levine,Manuel Aparicio IV
Neural Networks for Knowledge Representation and Inference Book PDF

Author : Daniel S. Levine,Manuel Aparicio IV
Publisher : Psychology Press
Page : 526 pages
File Size : 44,9 Mb
Release : 2013-04-15
Category : Psychology
ISBN : 9781134771615

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Neural Networks for Knowledge Representation and Inference by Daniel S. Levine,Manuel Aparicio IV Pdf

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

Daniel S. Levine,Manuel Aparicio IV
Neural Networks for Knowledge Representation and Inference Book PDF

Author : Daniel S. Levine,Manuel Aparicio IV
Publisher : Psychology Press
Page : 523 pages
File Size : 42,7 Mb
Release : 2013-04-15
Category : Psychology
ISBN : 9781134771547

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Neural Networks for Knowledge Representation and Inference by Daniel S. Levine,Manuel Aparicio IV Pdf

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

I. Tiddi,F. Lécué,P. Hitzler
Knowledge Graphs for eXplainable Artificial Intelligence Foundations Applications and Challenges Book PDF

Author : I. Tiddi,F. Lécué,P. Hitzler
Publisher : IOS Press
Page : 314 pages
File Size : 48,9 Mb
Release : 2020-05-06
Category : Computers
ISBN : 9781643680811

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Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges by I. Tiddi,F. Lécué,P. Hitzler Pdf

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

Ian Cloete,Jacek M. Zurada
Knowledge based Neurocomputing Book PDF

Author : Ian Cloete,Jacek M. Zurada
Publisher : MIT Press
Page : 512 pages
File Size : 48,8 Mb
Release : 2000
Category : Computers
ISBN : 0262032740

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Knowledge-based Neurocomputing by Ian Cloete,Jacek M. Zurada Pdf

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

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Nikola K. Kasabov
Time Space Spiking Neural Networks and Brain Inspired Artificial Intelligence Book PDF

Author : Nikola K. Kasabov
Publisher : Springer
Page : 738 pages
File Size : 46,6 Mb
Release : 2018-08-29
Category : Technology & Engineering
ISBN : 9783662577158

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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence by Nikola K. Kasabov Pdf

Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.

Neural-Symbolic Cognitive Reasoning

Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay
Neural Symbolic Cognitive Reasoning Book PDF

Author : Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay
Publisher : Springer Science & Business Media
Page : 200 pages
File Size : 48,5 Mb
Release : 2009
Category : Computers
ISBN : 9783540732457

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Neural-Symbolic Cognitive Reasoning by Artur S. D'Avila Garcez,Luís C. Lamb,Dov M. Gabbay Pdf

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.

Neuro-Symbolic Artificial Intelligence: The State of the Art

P. Hitzler
Neuro Symbolic Artificial Intelligence The State of the Art Book PDF

Author : P. Hitzler
Publisher : IOS Press
Page : 410 pages
File Size : 44,9 Mb
Release : 2022-01-19
Category : Computers
ISBN : 9781643682457

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Neuro-Symbolic Artificial Intelligence: The State of the Art by P. Hitzler Pdf

Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.

Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning

Hua Shi,Hu-Chen Liu
Fuzzy Petri Nets for Knowledge Representation Acquisition and Reasoning Book PDF

Author : Hua Shi,Hu-Chen Liu
Publisher : Springer Nature
Page : 476 pages
File Size : 41,9 Mb
Release : 2023-10-23
Category : Technology & Engineering
ISBN : 9789819951543

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Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning by Hua Shi,Hu-Chen Liu Pdf

This book provides valuable knowledge, useful fuzzy Petri nets (FPN) models, and practical examples that can be considered by mangers in supporting knowledge management of organizations to increase and sustain their competitive advantages. In this book, the authors proposed various improved FPN models to enhance the modeling power and applicability of FPNs in knowledge representation and reasoning. This book is useful for practitioners and researchers working in the fields of knowledge management, operation management, information science, industrial engineering, and management science. It can also be used as a textbook for postgraduate and senior undergraduate students.

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering

Nikola K. Kasabov
Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering Book PDF

Author : Nikola K. Kasabov
Publisher : Marcel Alencar
Page : 581 pages
File Size : 48,9 Mb
Release : 1996
Category : Artificial intelligence
ISBN : 9780262112123

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Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering by Nikola K. Kasabov Pdf

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.

An Introduction to Knowledge Engineering

Simon Kendal,Malcolm Creen
An Introduction to Knowledge Engineering Book PDF

Author : Simon Kendal,Malcolm Creen
Publisher : Springer Science & Business Media
Page : 294 pages
File Size : 51,5 Mb
Release : 2007-08-08
Category : Computers
ISBN : 9781846286674

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An Introduction to Knowledge Engineering by Simon Kendal,Malcolm Creen Pdf

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Knowledge-Based Systems

Rajendra Akerkar,Priti Sajja
Knowledge Based Systems Book PDF

Author : Rajendra Akerkar,Priti Sajja
Publisher : Jones & Bartlett Learning
Page : 375 pages
File Size : 43,7 Mb
Release : 2010-08-30
Category : Computers
ISBN : 9780763776473

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Knowledge-Based Systems by Rajendra Akerkar,Priti Sajja Pdf

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.

Associative Networks

N. V. Findler
Associative Networks Book PDF

Author : N. V. Findler
Publisher : Unknown
Page : 488 pages
File Size : 43,9 Mb
Release : 1979
Category : Computers
ISBN : UOM:39015000461700

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Associative Networks by N. V. Findler Pdf

Overview and general systems; Theoretically oriented efforts; Areas of application; .

Intelligent Spatial Decision Support Systems

Yee Leung
Intelligent Spatial Decision Support Systems Book PDF

Author : Yee Leung
Publisher : Springer Science & Business Media
Page : 477 pages
File Size : 53,9 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642607141

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Intelligent Spatial Decision Support Systems by Yee Leung Pdf

In the past half century, we have experienced two major waves of methodological development in the study of human behavior in space and time. The fIrst wave was the well known "quantitative revolution" which propelled geography from a mainly descriptive discipline to a scientifIc discipline using formalism such as probability, statistics, and a large-number of mathematical methods for analyzing spatial structures and processes under certainty and uncertainty. The second wave is the recent advancement of geographical information systems which equips geographers with automation in the storage, retrieval, analysis, and display of data. Both developments have significant impacts on geographical studies in general and solutions to real life spatio-temporal problems in particular. They have found applications in urban and regional planning, automated mapping and facilities management, transportation planning and management, as well as environmental planning and management, to name but a few examples. Both developments have one thing in common. They one way or the other use computer to process and analyze data. However, not until recently, there has been very little interaction between the two. Quantitative models have largely been developed independent of the underlying data models and structures representing the spatial phenomena or processes under study. Display of analysis results has been primitive in terms of the utilization of computer graphic technologies. Formal models, in addition to their technical difficulties, have poor capability in communication with users. Geographical information systems, on the other hand, have originally been developed with a slight intention to entertain powerful analytical models.

Artificial Intelligence and Neural Networks

Vasant Honavar,Leonard Merrick Uhr
Artificial Intelligence and Neural Networks Book PDF

Author : Vasant Honavar,Leonard Merrick Uhr
Publisher : Unknown
Page : 696 pages
File Size : 41,8 Mb
Release : 1994
Category : Computers
ISBN : UOM:39015017433254

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Artificial Intelligence and Neural Networks by Vasant Honavar,Leonard Merrick Uhr Pdf

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.

Design and Development of Expert Systems and Neural Networks

L. R. Medsker,Larry Medsker,Jay Liebowitz
Design and Development of Expert Systems and Neural Networks Book PDF

Author : L. R. Medsker,Larry Medsker,Jay Liebowitz
Publisher : Prentice Hall
Page : 296 pages
File Size : 41,7 Mb
Release : 1994
Category : Computers
ISBN : UOM:39015029084251

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Design and Development of Expert Systems and Neural Networks by L. R. Medsker,Larry Medsker,Jay Liebowitz Pdf

This book gives readers and practitioners the tools they need to develop appropriate applications and systems. It also explores managing and institutionalizing expert system development and usage.