Surprise Minimization Framework

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

• Surprise Minimizationconcept0.905
  Core principle: acting to maximize value is equivalent to minimizing surprise by sampling environment to conform to expectations.
• surprisal minimizationconcept0.801
  The principle that agents must minimise prediction error (surprisal) to persist.
• surprisal minimisationconcept0.772
  The core imperative under the Free Energy Principle; systems must reduce the difference between predicted and actual sensory states.
• Surprise (Surprisal)concept0.770
  Negative log probability of an outcome under the generative model; minimized in active inference.
• Error minimizationconcept0.756
  The progressive reduction of error (stress) as cells move toward their target positions.
• Ambiguity Minimizationconcept0.753
  The drive to reduce expected ambiguity about outcomes given states, leading to seeking well-lit, informative environments.
• Surpriseconcept0.745
  The negative log probability of sensory samples; minimized by free energy.
• Active inference can be framed as the minimisation of surprise by perception and action.claim0.739
  Concise characterisation from Section 2.