HYBRID NEURAL-SYMBOLIC SYSTEMS: INTEGRATING KNOWLEDGE REPRESENTATION AND DEEP LEARNING FOR COMPLEX PROBLEM SOLVING
DOI:
https://doi.org/10.29121/shodhai.v3.i1.2026.83Keywords:
Hybrid AI, Neural-Symbolic Systems, Knowledge Representation, Deep Learning,, Explainable AI, Neuro-Symbolic IntegrationAbstract
Hybrid neural-symbolic systems are neural networks integrated with symbolic reasoning systems, so that together they can be as powerful at pattern recognition in high dimensions as they are at structured knowledge representation and logical inference. This combination works to solve the issues with data-driven deep learning, including a lack of explainability and limited reasoning abilities, and helps overcome brittleness and low adaptability of purely symbolic AI. The paper compares the theoretical basis, architecture, and performance of hybrid neural-symbolic systems in real world complex problem-solving areas, such as healthcare diagnostics, legal reasoning, and planetary autonomous robotics. The new method combines constraint logic issues with deep learning models by using differentiable reasoning stages and neural representations guided by ontology principles. In low-data regimes, comparative experiments show greater interpretability, precision of reasoning, and generalization. Nevertheless, several practical challenges do still exist, notably the computational overhead, scale in large knowledge graphs, and knowledge engineering. Automated knowledge acquisition in future, efficient neuro-symbolic fusion techniques and real time reasoning in dynamic environments would be the areas of interest to work on.
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