The LNN-based inductive logic programming method proposed by Sen et al. (2022) operates through the following procedure: (1) Input a knowledge base containing facts, relations, and rules describing the target structure. (2) Build an LNN network based on the template to simulate logical connectives, where each node represents an expression or logical rule. (3) Use facts in the knowledge base as training data to adjust logical operations via optimization algorithms like back propagation and gradient descent. (4) Convert the trained LNN into a set of logical rules that reflect the relationships in the input data.

Authors

• Person: Not available Organization: arXiv
  Neuro-Symbolic AI: Explainability, Challenges, and Future Trends

Sources

• Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv via serper

Referenced by nodes (4)

• knowledge base concept
• gradient descent concept
• optimization algorithms concept
• logic rules concept