Skip to main content
Ctrl+K
Neuro-Symbolic Reasoning - Home Neuro-Symbolic Reasoning - Home
  • Neuro-Symbolic Reasoning: Fundamentals, Models, Certification, and Systems

Introduction

  • Introduction

Part I · Foundations

  • 1.1 The rise and fall of symbolic AI: from expert systems to the knowledge-engineering bottleneck
  • 2.1 Propositional logic and first-order predicate logic
  • 3.1 Basic concepts of knowledge graphs: entities, relations, attributes, events
  • 4.1 Basic ideas of deep representation learning

Part II · Domain Knowledge Modeling

  • 5.1 Object system for low-altitude traffic / UAM: aircraft, airspace, missions, infrastructure, stakeholders
  • 6.1 Ontology layering: concept layer, instance layer, rule layer

Part III · Hybrid Reasoning

  • 7.1 Classical taxonomies: Henry Kautz’s neuro-symbolic spectrum
  • 8.1 Logic rules as loss functions
  • 9.1 SPARQL queries and deterministic evidence extraction
  • 10.1 Defining static risk reasoning
  • 11.1 From static KG to temporal KG
  • 12.1 Formalizing multi-UAV dynamic coordination
  • 13.1 Conflict detection vs. conflict resolution

Part IV · Trustworthy Certification

  • 14.1 Why Neuro-Symbolic Systems Are Still Not Inherently Trustworthy
  • 15.1 Types of Uncertainty: Epistemic vs. Aleatoric
  • 16.1 Failure Modes in Online Inference
  • 17.1 Layers of Explainability: Local, Rule-Based, Causal

Part V · System Deployment

  • 18.1 Complexity in Symbolic, Graph, and LLM Reasoning
  • 19.1 Roles of Edge vs. Cloud Inference
  • 20.1 Spatiotemporal-Aware Graph Partitioning
  • 21.1 Integrating Knowledge Substrate, Reasoning Models, Certification, and Deployment

Part VI · Frontier

  • 22.1 The LLM as System 1: Strengths, Hallucinations, and Structural Limitations
  • 23.1 Intelligent Transportation and Scene-Graph Understanding for Autonomous Driving
  • 24.1 Molecular Graphs, Scientific Knowledge, and Interpretable Discovery
  • 25.1 Book Arc: Knowledge Substrate, Reasoning, Certification, Systems

Appendices

  • Appendix A — Mathematical, Logical and Graph Learning Notation
  • Appendix B — Open-Source Libraries and Engineering Tools
  • Appendix C — Course Labs and Practice Projects

Experiments

  • Appendix C — Experiment Code
    • Lab 1: Small Low-Altitude Ontology and Rule Encoding
    • Lab 2: Static Risk Reasoning with KG
    • Lab 3: Temporal KG Conflict Detection
    • Lab 4: Conformal Prediction and Uncertainty Calibration
    • Lab 5: Cloud-Edge Collaboration and Event-Driven Prototype
    • Lab 6: Neuro-Symbolic Agent Prototype
  • Repository
  • Open issue

Index

By Yushu Liu

© Copyright 2025.

Yushu Liu · yushuliu@outlook.com · GitHub
Text licensed under CC BY-NC-SA 4.0 · Code licensed under MIT