Sophisticated Inference

• Published in: Neural computation Vol. 33; no. 3; pp. 713 - 763
• Main Authors: Friston, Karl, Da Costa, Lancelot, Hafner, Danijar, Hesp, Casper, Parr, Thomas
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.03.2021; MIT Press Journals, The
• Subjects: First principles; Free energy; Functionals; Inference; Mathematical analysis
• ISSN: 0899-7667; 1530-888X; 1530-888X
• DOI: 10.1162/neco_a_01351

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