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| Author | : P. Hitzler |
| Publisher | : IOS Press |
| Total Pages | : 410 |
| Release | : 2022-01-19 |
| Genre | : Computers |
| ISBN | : 1643682458 |
Download Neuro-Symbolic Artificial Intelligence: The State of the Art Book in PDF, ePub and Kindle
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 | : Artur S. d'Avila Garcez |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 276 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447102118 |
Download Neural-Symbolic Learning Systems Book in PDF, ePub and Kindle
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 | : Tiansi Dong |
| Publisher | : Springer Nature |
| Total Pages | : 155 |
| Release | : 2020-08-24 |
| Genre | : Technology & Engineering |
| ISBN | : 3030562751 |
Download A Geometric Approach to the Unification of Symbolic Structures and Neural Networks Book in PDF, ePub and Kindle
The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged. Unfortunately, present embedding approaches cannot. The approach in this book makes the unification possible. It is indeed a new and promising approach in AI. -Bo Zhang, Director of AI Institute, Tsinghua It is indeed wonderful to see the reviving of the important theme Nural Symbolic Model. Given the popularity and prevalence of deep learning, symbolic processing is often neglected or downplayed. This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches. -Ron Sun (RPI), on Governing Board of Cognitive Science Society Both for language and humor, approaches like those described in this book are the way to snickerdoodle wombats. -Christian F. Hempelmann (Texas A&M-Commerce) on Executive Board of International Society for Humor Studies
| Author | : Alexiei Dingli |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 196 |
| Release | : 2023-05-31 |
| Genre | : Computers |
| ISBN | : 1804616958 |
Download Neuro-Symbolic AI Book in PDF, ePub and Kindle
Explore the inner workings of AI along with its limitations and future developments and create your first transparent and trustworthy neuro-symbolic AI system Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand symbolic and statistical techniques through examples and detailed explanations Explore the potential of neuro-symbolic AI for future developments using case studies Discover the benefits of combining symbolic AI with modern neural networks to build transparent and high-performance AI solutions Book Description Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you'll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You'll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions. What you will learn Gain an understanding of the intuition behind neuro-symbolic AI Determine the correct uses that can benefit from neuro-symbolic AI Differentiate between types of explainable AI techniques Think about, design, and implement neuro-symbolic AI solutions Create and fine-tune your first neuro-symbolic AI system Explore the advantages of fusing symbolic AI with modern neural networks in neuro-symbolic AI systems Who this book is for This book is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to explore the emerging field of neuro-symbolic AI and discover how to build transparent and trustworthy AI solutions. A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.
| Author | : P. Hitzler |
| Publisher | : IOS Press |
| Total Pages | : 706 |
| Release | : 2023-08-04 |
| Genre | : Computers |
| ISBN | : 1643684078 |
Download Compendium of Neurosymbolic Artificial Intelligence Book in PDF, ePub and Kindle
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 | : Artur S. D'Avila Garcez |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 200 |
| Release | : 2009 |
| Genre | : Computers |
| ISBN | : 3540732454 |
Download Neural-Symbolic Cognitive Reasoning Book in PDF, ePub and Kindle
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 | : John F. Sowa |
| Publisher | : Addison Wesley Publishing Company |
| Total Pages | : 504 |
| Release | : 1984 |
| Genre | : Computers |
| ISBN | : |
Download Conceptual Structures Book in PDF, ePub and Kindle
"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 |
| Total Pages | : 266 |
| Release | : 2008-01-01 |
| Genre | : Computers |
| ISBN | : 1846288398 |
Download Fundamentals of the New Artificial Intelligence Book in PDF, ePub and Kindle
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 | : Alonso, Eduardo |
| Publisher | : IGI Global |
| Total Pages | : 396 |
| Release | : 2010-11-30 |
| Genre | : Computers |
| ISBN | : 1609600231 |
Download Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications Book in PDF, ePub and Kindle
"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--
| Author | : Charu C. Aggarwal |
| Publisher | : Springer Nature |
| Total Pages | : 497 |
| Release | : 2021-07-16 |
| Genre | : Computers |
| ISBN | : 3030723577 |
Download Artificial Intelligence Book in PDF, ePub and Kindle
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.
