Under active inference, the ultimate ‘goal’ is to maintain a coherent phenotype and persist over time, not to maximize reward.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.845
  §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.claim0.839
  Abstract and §3, preference learning section.
• Active inference postulates that agents achieve survival by optimising two complementary objective functions, a variational free energy and an expected free energy.claim0.836
  Core claim of active inference stated in Section 2.
• In brief, active inference proposes that agents achieve this by optimising two complementary objective functions, a variational free energy and an expected free energy.quote0.833
  Concise statement of the core hypothesis from Section 2.
• In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.833
  Abstract; central distinction.
• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.823
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• Active inference treats exploration and exploitation as two different ways of tackling the same problem: minimizing uncertainty.claim0.821
  §2, comment on expected free energy decomposition.
• Active inference offers a degree of plausibility as a process theory of brain function.claim0.819
  Overall assessment from Discussion.