I'm Richard Hines, and I design policy-bound AI systems for education, manufacturing and clinical settings.
I am the discoverer of the Emergent State Machine (ESM), a control architecture that separates policy, state and generation in AI systems.
The Emergent State Machine (ESM)
A control architecture for AI systems that separates policy, state, and generation. Rather than allowing model outputs to directly determine system behavior, the ESM encodes declared rules and decision boundaries into a structured state machine. It was first developed in the context of learning systems, but its core principles apply wherever institutions need AI systems that are traceable, reproducible, and aligned with declared policy.
The Digital Learning Companion (DLC)
A structured learning architecture that delivers transparent, policy-aware feedback in math and language domains. By explicitly separating primitives, signals, and state updates, the system preserves clarity and auditability — ensuring that progress is authorized by declared rules, not opaque inference. AI can be integrated, but it is never required.