Neuro-Symbolic AI

Neuro-symbolic AI studies how to combine neural models, which learn patterns from data, with symbolic representations such as rules, logic, knowledge graphs, and plans. Common approaches include differentiable logic layers, rule-based or constraint-based checks around language models, and agents that use learned proposals with symbolic planners or verifiers. These methods are applied to tasks such as language formalization, clinical decision support, cybersecurity, and long-horizon robotics, and are tested on both domain benchmarks and real-world systems. A central challenge is balancing the flexibility of learned models with the precision of symbolic reasoning, especially when inputs are uncertain, rules are incomplete, or conditions differ from training data.