Sophisticated Inference
Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas, Parr

TL;DR
This paper introduces a recursive, belief-about-beliefs extension of active inference, enabling deep planning over future belief sequences, demonstrated through numerical simulations of complex decision tasks.
Contribution
It develops a recursive form of expected free energy in active inference, allowing agents to model beliefs about beliefs and perform deep belief-based planning.
Findings
Recursive free energy enables deep belief tree search.
Agents can model counterfactual beliefs about future states.
Numerical simulations demonstrate improved decision-making.
Abstract
Active inference offers a first principle account of sentient behaviour, from which special and important cases can be derived, e.g., reinforcement learning, active learning, Bayes optimal inference, Bayes optimal design, etc. Active inference resolves 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 paper, 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 and beliefs about those latent states. In other words, we…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Philosophy and History of Science · Game Theory and Applications
