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Title
Neuro symbolic reasoning and learning / Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala.
ISBN
9783031391798 (electronic bk.)
3031391799 (electronic bk.)
9783031391781
3031391780
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (xii, 119 pages) : illustrations (some color).
Item Number
10.1007/978-3-031-39179-8 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 21, 2023).
Series
SpringerBriefs in computer science, 2191-5776
Chapter1 New Ideas in Neuro Symbolic Reasoning and Learning
Chapter2 Brief Introduction to Propositional Logic and Predicate Calculus
Chapter3 Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence
Chapter4 LTN: Logic Tensor Networks
Chapter5 Neuro Symbolic Reasoning with Ontological Networks
Chapter6 LNN: Logical Neural Networks
Chapter7 NeurASP
Chapter8 Neuro Symbolic Learning with Differentiable Inductive Logic Programming
Chapter9 Understanding SATNet: Constraint Learning and Symbol Grounding
Chapter10 Neuro Symbolic AI for Sequential Decision Making
Chapter11 Neuro Symbolic Applications.
