In discrete state-space models, agents select from different possible policies to realise their preferences and minimise the surprise that they expect to encounter in the future.

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• Discrete State-Space Modelsconcept0.804
  Mathematical formalism used in active inference for modeling hierarchical and discrete brain processes.
• Agents perceive by minimizing variational free energy to ensure model consistency with past observations and act by minimizing expected free energy to make future sensations consistent with preferences.claim0.784
  Formalization of perception-action cycle integrating inference and decision-making.
• This paper provides a complete mathematical synthesis of active inference on discrete state-space models.claim0.773
  Self-description of the paper in the abstract.
• A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model's power).claim0.771
  Prediction orthogonality thesis.
• Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.764
  Opening sentence defining self-evidencing.
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.761
  Abstract and §3, preference learning section.
• In response to disequilibrium either within an agent's own cognitive organization or between the agent's cognitive state and other internal dynamics, a person's current frame of mind can undergo a phase change and recalibrate to rectify the disequilibrium.concept0.756
• Whether a state is rewarding (or not) is a function of the agent themselves, and not the environment.claim0.755
  §1, contrasting RL reward conceptualization.