Neuro-Symbolic Artificial Intelligence: The State of the Art

P. Hitzler, M.K. Sarker, Md Kamruzzaman Sarker

• Publisher: SAGE Publications Ltd
• Year: 2022
• Print ISBN:
  9781643682440
• Edition: 1
• eBook ISBN:
  9781643682457
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

Table of contents

• Title Page
• Preface: The 3rd AI wave is coming, and it needs a theory
• Introduction
• Contents
• Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
• Chapter 2. Symbolic Reasoning in Latent Space: Classical Planning as an Example
• Chapter 3. Logic Meets Learning: From Aristotle to Neural Networks
• Chapter 4. Graph Reasoning Networks and Applications
• Chapter 5. Answering Natural-Language Questions with Neuro-Symbolic Knowledge Bases
• Chapter 6. Tractable Boolean and Arithmetic Circuits
• Chapter 7. Neuro-Symbolic AI = Neural + Logical + Probabilistic AI
• Chapter 8. A Constraint-Based Approach to Learning and Reasoning
• Chapter 9. Spike-Based Symbolic Computations on Bit Strings and Numbers
• Chapter 10. Explainable Neuro-Symbolic Hierarchical Reinforcement Learning
• Chapter 11. Neuro-Symbolic Semantic Reasoning
• Chapter 12. Learning Reasoning Strategies in End-to-End Differentiable Proving
• Chapter 13. Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
• Chapter 14. Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning
• Chapter 15. Human-Centered Concept Explanations for Neural Networks
• Chapter 16. Abductive Learning
• Chapter 17. Logic Tensor Networks: Theory and Applications
• Author Index