Pith. sign in

REVIEW 1 cited by

Whence the Expected Free Energy?

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2004.08128 v5 pith:AUAHP3ZH submitted 2020-04-17 cs.AI

classification cs.AI
keywords energyfreeactiveexpectedfutureinferencequantityagents
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The Expected Free Energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to the balance of exploration and exploitation that active inference agents evince. Despite its importance, the mathematical origins of this quantity and its relation to the Variational Free Energy (VFE) remain unclear. In this paper, we investigate the origins of the EFE in detail and show that it is not simply "the free energy in the future". We present a functional that we argue is the natural extension of the VFE, but which actively discourages exploratory behaviour, thus demonstrating that exploration does not directly follow from free energy minimization into the future. We then develop a novel objective, the Free-Energy of the Expected Future (FEEF), which possesses both the epistemic component of the EFE as well as an intuitive mathematical grounding as the divergence between predicted and desired futures.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Free Energy Projective Simulation (FEPS): Active inference with interpretability

    cs.AI 2024-11 conditional novelty 6.0 of 10

    FEPS agents combine projective simulation with active inference to learn world models and goal-directed policies from prediction accuracy alone, resolving ambiguous observations without external rewards.

Pith tools