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Compendium of neurosymbolic artificial intelligence /

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Bibliographic Details
Corporate Author: IOS Press
Other Authors: Hitzler, Pascal (Editor), Sarker, Md Kamruzzaman (Editor), Eberhart, Aaron (Editor)
Format: Electronic eBook
Language:English
Published: Amsterdam : IOS Press, [2023]
Subjects:
Artificial intelligence.
Neural networks (Computer science)
artificial intelligence.
Online Access: Full text (MFA users only)
ISBN:9781643684079
1643684078
Local Note:ProQuest Ebook Central
Table of Contents:
  • Intro
  • Title Page
  • Introduction
  • Contents
  • Chapter 1. The Roles of Symbols in Neural-Based AI: They Are Not What You Think!
  • Chapter 2. Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
  • Chapter 3. Architectural Patterns for Neuro-Symbolic AI
  • Chapter 4. Semantic Web Machine Learning Systems: An Analysis of System Patterns
  • Chapter 5. Boolean Connectives and Deep Learning: Three Interpretations
  • Chapter 6. Constructivist Machine Learning
  • Chapter 7. Neural-Symbolic Interaction and Co-Evolving
  • Chapter 8. Neuro-Causal Models
  • Chapter 9. Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models
  • Chapter 10. Connectionist Neuroarchitectures in Cognition and Consciousness Theory Based on Integrative (Synchronization) Mechanisms
  • Chapter 11. Autodidactic and Coachable Neural Architectures
  • Chapter 12. The Neural Blackboard Theory of Neuro-Symbolic Processing: Logistics of Access, Connection Paths and Intrinsic Structures
  • Chapter 13. Class Expression Learning with Multiple Representations
  • Chapter 14. Embedding-Based First-Order Rule Learning in Large Knowledge Graphs
  • Chapter 15. Lifted Relational Neural Networks: From Graphs to Deep Relational Learning
  • Chapter 16. Discovering Visual Concepts and Rules in Convolutional Neural Networks
  • Chapter 17. Approximate Answering of Graph Queries
  • Chapter 18. Enhancing Case-Based Reasoning with Neural Networks
  • Chapter 19. Neuro-Symbolic Spatio-Temporal Reasoning
  • Chapter 20. Neuro-Symbolic Architectures for Combinatorial Problems in Structured Output Spaces
  • Chapter 21. Neuro-Symbolic Semantic Learning for Chemistry
  • Chapter 22. Semantic Loss Functions for Neuro-Symbolic Structured Prediction
  • Chapter 23. Combining Symbolic and Deep Learning Approaches for Sentiment Analysis
  • Chapter 24. Few-Shot Continual Learning Based on Vector Symbolic Architectures
  • Chapter 25. Learning Logic Explanations by Neural Networks
  • Chapter 26. Combining Sub-Symbolic and Symbolic Methods for Explainability
  • Chapter 27. Explaining CNNs Using Knowledge Extraction and Graph Analysis
  • Chapter 28. Effective Reasoning over Neural Networks Using Lukasiewicz Logic
  • Chapter 29. Latent Trees for Compositional Generalization
  • Chapter 30. Weakly Supervised Reasoning by Neuro-Symbolic Approaches

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