claim

active

claim:active-inference-postulates-that-agents-achieve-survival-by-optimising-two-complementary-objective-functions-a-variational-free-energy-and-an-expected-free-energy

Active inference postulates that agents achieve survival by optimising two complementary objective functions, a variational free energy and an expected free energy.

Core claim of active inference stated in Section 2.

Source paper

extracted_from

Active inference on discrete state-spaces: a synthesis

(2020) · Lancelot Da Costa · Thomas Parr · Noor Sajid · Sebastijan Veselic +2

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In brief, active inference proposes that agents achieve this by optimising two complementary objective functions, a variational free energy and an expected free energy.quote0.929
Concise statement of the core hypothesis from Section 2.
Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.840
Abstract and §3, preference learning section.
Active inference has no random or stochastic aspects; everything changes to minimize variational free energy in accord with Hamilton's principle of least action.claim0.840
Ontological claim about the deterministic nature of active inference agents in these simulations
Under active inference, the ultimate ‘goal’ is to maintain a coherent phenotype and persist over time, not to maximize reward.claim0.836
§3, preference learning discussion.
Any system that exists will appear to minimize free energy and therefore engage in active inference.claim0.833
The reworked argument that free energy minimization is a corollary of existence, not a prerequisite.
Biological agents use a process theory of active inference where neuronal dynamics correspond to variational free energy minimisation for perception and expected free energy minimisation for action.hypothesis0.832
The core process theory hypothesis set up in the paper.
By minimising variational free energy, agents perform approximate Bayesian inference.claim0.830
Describes the epistemic function of variational free energy.
Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.826
Abstract and §1, summarizing a key property.