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September 20, 2025

Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era

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Authors

GDGiovanni Pio DelvecchioUniversity of BolognaLMLorenzo MolfettaUniversity of BolognaGMGianluca MoroUniversity of Bologna

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Implication

This survey explores task-specific advancements in neuro-symbolic artificial intelligence, highlighting explainability and reasoning capabilities.

Key Points

  • Neuro-symbolic methods enhance explainability and reasoning in AI applications, improving upon traditional models.
  • The study identifies significant advancements in natural language processing and computer vision using neuro-symbolic techniques.
  • Challenges in semantic generalizability and complexity hinder the practical application of neuro-symbolic systems in real-world scenarios.
  • This survey serves as a resource for those investigating explainable neuro-symbolic methodologies for real-life tasks.

Cite This Study

Delvecchio et al. (2025) studied this question.

synapsesocial.com/papers/68d43913713b0b5dfea7914fhttps://doi.org/10.24963/ijcai.2025/1157
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  5. 5Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems2025