text: **Core Concepts**
  
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              [html]<thead><tr><td>AIF</td><td>Active inference is a formal framework for modelling behaviour and cognition. Perception and action are cast as minimising free energy — the V / E and EFE, respectively — given a generative model of the environment.</td></tr></thead><tbody><tr><td>VFE</td><td>The watational free energy. 7 quantities how well a generative model explains incoming sensery observation. It can be revertites as the regative lag model evidence ( culted surprise ) opper - bounded by the divergence from the optimal pom</td></tr><tr><td>FW ( s ),+)</td><td>EFE</td></tr><tr><td>The expoted from energy. C quantifies the expected future free menty under an action policy ±. It consists of an information gain term and a prognativ value term that provide a natural balance between explinatory and goal - seeking behavio</td><td>GS = − Rq ( 0.8 ) n1 [ ln4 ( 5 ) i, τ ) − ln4 ( s ) m ] − Rq ( 0 ( r )] h p ( d ( c )]</td></tr><tr><td>Information gain</td><td>Pragmatic value</td></tr><tr><td>Generative model</td><td>The generative model is an agent ’ s formal assumptions about the structure and dynamics of its environment, hased on which perceptual and active inferences are carried out. Many types of generative models exist that are suitable for dife</td></tr></tbody>
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