Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.

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• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.902
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• 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.claim0.873
  Sets the theoretical grounding in Section 2.
• Active inference has no random or stochastic aspects; everything changes to minimize variational free energy in accord with Hamilton's principle of least action.claim0.854
  Ontological claim about the deterministic nature of active inference agents in these simulations
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.849
  §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.claim0.847
  Abstract and §3, preference learning section.
• Active inference provides a framework (derived from first principles) for solving and understanding the behavior of autonomous agents.claim0.846
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.840
  Abstract and §1, summarizing a key property.
• Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents.quote0.839