The Needed Bridge Connecting Symbolic and Sub-Symbolic AI

Innovations that combine the interpretability of symbolic AI with the learning capabilities of sub-symbolic AI can flourish in the nexus of symbolic and sub-symbolic AI. This research presents Fuzzy Cognitive Maps (FCMs). This hybrid model combines the best features of both paradigms as a workable answer to the problems of interpretability and explainability in artificial intelligence (AI) systems. FCMs have become a robust framework for logically and intuitively supporting decision-making processes and expressing causal information. A more organic and adaptable problem-solving approach is made possible by FCMs’ ability to manage the inherent ambiguity and uncertainty present in real-world situations. Because of their innate flexibility and ability to learn and adapt from sub-symbolic AI, FCMs are an excellent fit for applications requiring high interpretability and explainability.