Sophisticated Inference

Active inference offers a first principle account of sentient behavior, from which special and important cases—for example, reinforcement learning, active Bayes optimal inference, design—can be derived. finesses the exploitation-exploration dilemma in relation to prior preferences by placing information gain on same footing as reward or value. In brief, replaces value functions with functionals (Bayesian) beliefs, form an expected (variational) free energy. this letter, we consider sophisticated kind using recursive Sophistication describes degree agent has beliefs about beliefs. We agents counterfactual consequences action for states affairs those latent states. other words, move simply considering “what would happen if I did that” believe what that.” The energy functional effectively implements deep tree search over actions outcomes future. Crucially, is sequences belief opposed per se. illustrate competence scheme numerical simulations decision problems.