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About this book
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 priorknowledge 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.
Table of contents (11 chapters)
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Front Matter
Authors and Affiliations
About the authors
Gerardo I. Simari is a professor at UNS, and a researcher at CONICET. His research focuses on AI and Databases, and reasoning under uncertainty. He received a PhD in computer science from University of Maryland College Park and later joined the Department of Computer Science, University of Oxford, where he was also a Fulford Junior Research Fellow of Somerville College.
Chitta Baral is a Professor at the Arizona State University, and a past President of KR Inc. His research interests include Knowledge Representation and Reasoning, NLP and Image Understanding and often involves combining logical reasoning with explicit knowledge and neural learning and reasoning with textual and perceptual inputs.
Bowen Xi is a Ph.D. student at Arizona State University, specializing in the field of Neural Symbolic AI. She is passionate about combining the strengths of neural networks and symbolic reasoning to advance the field of artificial intelligence. Bowen's research interests include developing novel algorithms and techniques that enable machines to learn and reason like humans.
Lahari Pokala is a student pursuing her Master's degree at Arizona State University, where she is majoring in Computer Science. Her interests lie in artificial intelligence and data engineering.
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Bibliographic Information
Book Title: Neuro Symbolic Reasoning and Learning
Authors: Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-031-39179-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Softcover ISBN: 978-3-031-39178-1Published: 14 September 2023
eBook ISBN: 978-3-031-39179-8Published: 13 September 2023
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XII, 119
Number of Illustrations: 8 b/w illustrations, 10 illustrations in colour
Topics: Artificial Intelligence, Machine Learning
Keywords
- Artificial intelligence
- Machine Learning
- Deep Learning
- Fuzzy Logic
- Neural networks
- Neuro symbolic artificial intelligence
- Logic programming
- Knowledge Representation and Reasoning
- Inductive logic programming
- Natural language processing
- Reinforcement learning
- Answer Set Programming
- Symbol grounding
- Symbolic artificial intelligence
- Visual question answering