Neuro Symbolic Reasoning And Learning

Author : Artur S. d'Avila Garcez
Publisher : Springer Science & Business Media
ISBN 13 : 1447102118
Total Pages : 276 pages
Book Rating : 4.13/5 ( download)

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Download or read book Neural-Symbolic Learning Systems written by Artur S. d'Avila Garcez and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.

Author : Artur S. D'Avila Garcez
Publisher : Springer Science & Business Media
ISBN 13 : 3540732454
Total Pages : 200 pages
Book Rating : 4.57/5 ( download)

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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.

Author : P. Hitzler
Publisher : IOS Press
ISBN 13 : 1643682458
Total Pages : 410 pages
Book Rating : 4.57/5 ( download)

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Download or read book Neuro-Symbolic Artificial Intelligence: The State of the Art written by P. Hitzler and published by IOS Press. This book was released on 2022-01-19 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Paulo Shakarian
Publisher : Springer
ISBN 13 : 9783031391781
Total Pages : 0 pages
Book Rating : 4.80/5 ( download)

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Download or read book Neuro Symbolic Reasoning and Learning written by Paulo Shakarian and published by Springer. This book was released on 2023-09-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the key results and frameworks for various NSAO tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.

Author : SWARAT CHAUDHURI; KEVIN ELLIS; OLEKSANDR POLOZOV
Publisher :
ISBN 13 : 9781680839357
Total Pages : pages
Book Rating : 4.57/5 ( download)

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Download or read book NEUROSYMBOLIC PROGRAMMING written by SWARAT CHAUDHURI; KEVIN ELLIS; OLEKSANDR POLOZOV and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neurosymbolic programming is an emerging area that bridges the areas of deep learning and program synthesis. As in classical machine learning, the goal is to learn functions from data. However, these functions are represented as programs that can use neural modules in addition to symbolic primitives and are induced using a combination of symbolic search and gradient-based optimization. Neurosymbolic programming can offer multiple advantages over end-to-end deep learning. Programs can sometimes naturally represent long-horizon, procedural tasks that are difficult to perform using deep networks. Neurosymbolic representations are also, commonly, easier to interpret and formally verify than neural networks. The restrictions of a programming language can serve as a form of regularization and lead to more generalizable and data-efficient learning. Compositional programming abstractions can also be a natural way of reusing learned modules across learning tasks. In this monograph, the authors illustrate these potential benefits with concrete examples from recent work on neurosymbolic programming. They also categorize the main ways in which symbolic and neural learning techniques come together in this area and conclude with a discussion of the open technical challenges in the field. The comprehensive review of neurosymbolic programming introduces the reader to the topic and provides an insightful treatise on an increasingly important topic at the intersection of programming languages and machine learning. p learning or verification.

Author : P. Hitzler
Publisher : IOS Press
ISBN 13 : 1643684078
Total Pages : 706 pages
Book Rating : 4.79/5 ( download)

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Download or read book Compendium of Neurosymbolic Artificial Intelligence written by P. Hitzler and published by IOS Press. This book was released on 2023-08-04 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.

Author : Charu C. Aggarwal
Publisher : Springer Nature
ISBN 13 : 3030723577
Total Pages : 497 pages
Book Rating : 4.76/5 ( download)

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Download or read book Artificial Intelligence written by Charu C. Aggarwal and published by Springer Nature. This book was released on 2021-07-16 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence. The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.

Author : John F. Sowa
Publisher : Addison Wesley Publishing Company
ISBN 13 :
Total Pages : 504 pages
Book Rating : 4.67/5 ( download)

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Download or read book Conceptual Structures written by John F. Sowa and published by Addison Wesley Publishing Company. This book was released on 1984 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book combines the AI and cognitive sciences approaches. In combing insights from each of the separate fields, the book gives a unified view of knowledge representation." -- Preface.

Author : Toshinori Munakata
Publisher : Springer Science & Business Media
ISBN 13 : 1846288398
Total Pages : 266 pages
Book Rating : 4.95/5 ( download)

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Download or read book Fundamentals of the New Artificial Intelligence written by Toshinori Munakata and published by Springer Science & Business Media. This book was released on 2008-01-01 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows. General scope of the book Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems.

Author : Paulo Shakarian
Publisher : Springer Nature
ISBN 13 : 3031391799
Total Pages : 125 pages
Book Rating : 4.98/5 ( download)

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Download or read book Neuro Symbolic Reasoning and Learning written by Paulo Shakarian and published by Springer Nature. This book was released on 2023-10-15 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.