Principles of Active Inference
Active Inference, popularized by Karl Friston, is a framework from theoretical neuroscience explaining how biological systems maintain homeostasis and goal-directed behavior. It posits that agents act to minimize "variational free energy," which is equivalent to minimizing prediction error or surprise.
Core Theoretical Concepts
- Generative Models: Agents maintain internal representations of the causal structure of their environment.
- Bayesian Belief Updating: Perception is modeled as the update of internal priors based on sensory evidence.
- Epistemic Value: Actions are chosen not only to satisfy goals but to gather information that reduces uncertainty about the environment.
Cybernetic Foundations
- Ashby's Law of Requisite Variety: States that the variety in a controller must be at least as great as the variety of the system it is trying to control.
- The Good Regulator Theorem: Posits that every good regulator of a system must be a model of that system.
- Homeostasis & Allostasis: Biological principles of maintaining stability through predictive and reactive adjustments, which Active Inference formalizes mathematically.
- Perceptual Control Theory (PCT): A psychological framework stating that organisms act to control their perceptions rather than their behavior.
Autonomous Agent Research
Organizations such as VERSES AI are researching the commercialization of these principles through autonomous agents that operate based on spatial and temporal constraints.
Strategic Value
Active Inference provides a mathematical framework for moving beyond reactive token prediction. It establishes a foundation for proactive agents that exhibit curiosity and exploration while satisfying long-term logical constraints.