Active inference provides a framework (derived from first principles) for solving and understanding the behavior of autonomous agents.

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• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.847
• "Active inference is a self-organising process of action policy selection."quote0.847
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.846
  §3, after non-stationary results.
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.846
  §1, listing contributions.
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.844
  Abstract and §3, preference learning section.
• Active inference describes the dynamics of systems that persist at non-equilibrium steady-state and that can be statistically segregated from their environment via a Markov blanket.claim0.843
  Sets the theoretical grounding in Section 2.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.837
  Abstract and §1, summarizing a key property.
• Active inference offers a degree of plausibility as a process theory of brain function.claim0.831
  Overall assessment from Discussion.