Bayesian model expansion allows for generalisation and concept learning in active inference.

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• Bayesian Model Expansionmethod0.853
  Adding new states or parameters to the generative model if it increases model evidence, enabling concept learning.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.834
  §3, after non-stationary results.
• The key challenge for active inference is finding the generative model that best explains observable data.claim0.813
  Identifies an outstanding problem, Section 10.
• Active inference treats exploration and exploitation as two different ways of tackling the same problem: minimizing uncertainty.claim0.809
  §2, comment on expected free energy decomposition.
• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.809
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.799
  Abstract and §1, summarizing a key property.
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.796
  §1, listing contributions.
• How can active inference be scaled to complex models with many degrees of freedom while maintaining tractable inference?question0.791
  Another scaling question from Discussion.