Epistemology & Knowledge Representation

How agents "know" things — represent beliefs, and update them via Active Inference and rigorous epistemology.

1. Knowledge Representation (Ontologies & Graphs)

Knowledge is structured relationships between entities, not just text embeddings.

Knowledge Graph Augmentation: maintain a structured Knowledge Graph (nodes = entities, edges = relations). Don't rely on vector similarity (RAG) alone — use hybrid retrieval: vector search (semantic) + graph traversal (logical).

Query "What causes System Failure?": vector finds docs mentioning "crash"/"bug"; graph traverses System Failure <-caused_by- Memory Leak.

2. Active Inference (The Free Energy Principle)

Agents act to minimize Surprise (Variational Free Energy) = gap between expectation and sensation. Goal isn't maximizing reward but aligning the internal model with reality.

Prediction Error Minimization: (1) predict next observation from model; (2) act/sense actual outcome; (3) on surprise (obs != prediction), either update model (perceptual learning) or change world to match prediction (active inference).