Neuro-Symbolic AI in 2024: A Systematic Review
2.1. Taxonomy of Neuro-Symbolic AI
We identified five foundational research areas advancing the state of the art in Neuro-Symbolic AI. This taxonomy was synthesized from a review of six survey papers [20, 21, 22, 23, 24, 25] and four seminal books [2, 26, 27, 28]. These areas are:
Knowledge Representation: Integrating symbolic and neural representations and developing commonsense and domain-specific knowledge graphs [20, 26, 28].
Learning and Inference: Combining learning and reasoning processes through end-toend differentiable reasoning and dynamic multi-source knowledge reasoning [21, 22, 2].
Explainability and Trustworthiness: Creating interpretable models and reasoning processes to ensure trust and reliability in Neuro-Symbolic systems [23, 24] .
Logic and Reasoning: Integrating logic-based methods with neural networks, including logical and probabilistic reasoning, and the syntax and semantics of Neuro-Symbolic systems [23, 28].
Meta-Cognition: The system’s capacity to monitor, evaluate, and adjust its own reasoning and learning processes by integrating neural networks and symbolic representations.
The four above categories represent the core technical areas where current efforts are concentrated.
we define Meta-Cognition to address a gap in current taxonomies that fail to capture fields encompassing self-awareness, adaptive learning, reflective reasoning, self-regulation, and introspective monitoring.
Additionally, we define Meta-Cognition to address a gap in current taxonomies that fail to capture fields encompassing self-awareness, adaptive learning, reflective reasoning, self-regulation, and introspective monitoring.
Present research within Neuro- Symbolic AI does not yet effectively cover meta-cognition and neglecting Meta-Cognition in Neuro-Symbolic AI research limits system autonomy, adaptability, and reliability