Terms:

Classical model of action:

Optimal action depends on state of the world
Therefore, first step of action is to (1) form a belief (analyse surroundings/prospects)
(2) imagine a value function of next state brought about by action
(3) optimise action that maximises value of the next state

Model of action

Classical model doesn’t work when the best next thing to do is to search for/resolve uncertainty
Optimal action depends on beliefs about the world, and subsequent action
Further, it’s a function of the order in which you interrogate the world
Therefore the functional (function of a function) to be optimised is a function of beliefs
Optimal action therefore is optimising sequences or policies of actions
To be optimised: a function of a belief, integrated over time

Free Energy Principle:

The goal of a self-organising (eg biological) system is to minimise prediction error (surprise), also called ‘free energy’, by forming continually-updated beliefs/inferences about the world from which to form policies of action
Friston considers this an organising principle of all life and intelligence
To be alive (to be a system that resists disorder and dissolution) is to act in ways that reduce the gulf between your expectations and your sensory inputs (AKA, to minimise free energy)
If a prototypical agent, or a ‘good agent’ minimises free energy (thereby minimising ‘surprise’), they must believe that the actions they take minimised expected free energy
expected free energy associated with a policy of action is minimised

Markov Blanket:

The Markov Blanket is a concept in machine learning which is essentially a shield that separates one set of variables from others in a layered, hierarchical system. The blanket defines the boundaries of a given system. That is, in cognition, a cognitive version of a cell membrane shielding states inside the blanket from states outside. This is the schema by which surprise is minimised— the Markov blanket is a set of variables sufficiently complete that another random variable can be inferred from it . If a Markov blanket is minimal (parsimonious) (cannot drop any variable without losing information), it is called a Markov boundary.