The neural symbolic framework proposed by Kimura et al. (2021) for text-based games follows a multi-step process: (1) a semantic parser extracts basic propositional logic from text observations in the environment, converting natural language into logical expressions; (2) external knowledge bases like ConceptNet are used to understand word semantic categories and refine the extracted propositional logic; (3) the refined logic and lexical category information are combined via a First Order Logic (FOL) converter into logical facts representing game state conditions; (4) these logical facts are used as training input for a Logical Neural Network (LNN).

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 (3)

• Logical Neural Networks concept
• natural language concept
• Semantic Parsing concept