Part 4 - Symbolic AI and Knowledge Representation

While deep learning and LLMs dominate modern AI, symbolic AI techniques remain valuable for problems that require explicit reasoning, verifiable logic, and structured knowledge. In this part we explore both classical symbolic AI and practical knowledge representation.

We cover graph search and pathfinding algorithms, frame-based knowledge representation implemented in TypeScript, MiniZinc for constraint satisfaction (called from TypeScript via child processes), and knowledge representation using graph databases, relational databases, and Semantic Web technologies including RDF, SPARQL, and linked data.