Neuro-symbolic Artificial Intelligence The State Of The Art Pdf (2026)

An extension of the probabilistic logic programming language ProbLog. It integrates deep learning by treating neural network outputs as probabilistic facts within a logical reasoning pipeline, allowing for end-to-end gradient-based learning.

For the past decade, Deep Learning (DL) has dominated the artificial intelligence landscape. From large language models (LLMs) like GPT-4 to advanced computer vision systems, deep neural networks have achieved historic milestones. However, as these systems scale, their fundamental limitations become increasingly apparent: a lack of inherent logic, poor explainability, high data dependency, and a tendency to "hallucinate" or fail catastrophically in unfamiliar environments. An extension of the probabilistic logic programming language

Feldstein et al. (2024) present the first architecture‑based mapping of neuro‑symbolic techniques. The key insight is that different architectural families bring distinct strengths: From large language models (LLMs) like GPT-4 to

If you are searching for a comprehensive , the best sources are academic databases like IEEE Xplore, arXiv, or recent literature surveys focusing on neuro-symbolic AI architectures. Such documents typically provide: In-depth comparison of neural-symbolic integration methods. Detailed case studies. as these systems scale

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