Temporal discounting emerges naturally from active inference without an explicit discount factor, because predictions in the distant future are less precise.

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

• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.819
  Abstract and §3, preference learning section.
• In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.814
  Abstract; central distinction.
• Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.805
  Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.
• These dynamics are consistent with predictive processing since active inference prescribes dynamics that minimise prediction error.concept0.800
  Connection between active inference neuronal dynamics and predictive processing theory.
• The natural curiosity emerging in active inference contrasts with handcrafted exploration in reinforcement learning such as epsilon-greedy or ad hoc novelty bonuses.claim0.789
  §2, comparing exploration mechanisms.
• Any system that exists will appear to minimize free energy and therefore engage in active inference.claim0.786
  The reworked argument that free energy minimization is a corollary of existence, not a prerequisite.
• Under active inference, the ultimate ‘goal’ is to maintain a coherent phenotype and persist over time, not to maximize reward.claim0.783
  §3, preference learning discussion.
• Can active inference agents learn their own prior preferences without explicit reward signals?question0.783
  Question answered by the preference learning experiments.