Epistemic behavior (exploration) emerges from maximizing mutual information between hidden states and observations.

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

• Epistemic Foraging / Explorationconcept0.806
  The active sampling of observations to maximize information gain and resolve uncertainty about the environment.
• Epistemic Explorationconcept0.795
  Bayes-optimal exploration driven by uncertainty minimization; natural behavior in active inference without handcrafted mechanisms.
• Active inference agents can learn epistemic habits in the absence of extrinsic rewards through ambiguity minimization.claim0.789
  §2, summarizing information-seeking behavior.
• Active inference treats exploration and exploitation as two different ways of tackling the same problem: minimizing uncertainty.claim0.789
  §2, comment on expected free energy decomposition.
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.787
  §1, listing contributions.
• Epistemic Behaviorconcept0.786
  World-disclosing behavior that resolves uncertainty; driven by epistemic value and novelty components of expected free energy
• Curiosity manifests as active sampling of the world to minimize uncertainty about hypotheses for states of the world, driven by novelty-seeking policies that resolve ignorance about contingencies.claim0.785
  Formal definition of curiosity within active inference framework
• The natural curiosity emerging in active inference contrasts with handcrafted exploration in reinforcement learning such as epsilon-greedy or ad hoc novelty bonuses.claim0.783
  §2, comparing exploration mechanisms.