claim

active

claim:curiosity-manifests-as-active-sampling-of-the-world-to-minimize-uncertainty-about-hypotheses-for-states-of-the-world-driven-by-novelty-seeking-policies-that-resolve-ignorance-about-contingencies

Curiosity manifests as active sampling of the world to minimize uncertainty about hypotheses for states of the world, driven by novelty-seeking policies that resolve ignorance about contingencies.

Formal definition of curiosity within active inference framework

Source paper

extracted_from

Active Inference, Curiosity and Insight

(2017) · Karl Friston · Marco Lin · Chris Frith · Giovanni Pezzulo +2

Neighborhood — ranked by edge-count

Findings (1)

finding

In early trials, right and left cue locations are more attractive than the central location despite being inherently ambiguous, because the agent knows it is ignorant and can resolve this through novelty exposure.
associated_withsupports
Demonstration that ignorance-driven novelty-seeking (not ambiguity avoidance) governs early exploration

Concepts (1)

concept

Curiosity
supports
Active sampling of novel contingencies to minimize uncertainty; formalized as novelty component of expected free energy

Questions (1)

question

Why do human beings devote so much time and effort to the acquisition of knowledge?
answered_by
First of Berlyne's (1954) framing questions; answered by curiosity as expected free energy minimization (novelty)

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

The natural curiosity emerging in active inference contrasts with handcrafted exploration in reinforcement learning such as epsilon-greedy or ad hoc novelty bonuses.claim0.829
§2, comparing exploration mechanisms.
Curiosity, insight, decision-making, and diverse phenomena can all be accommodated by a single imperative: minimization of expected free energy (resolution of uncertainty).claim0.807
Central thesis of the paper unifying cognitive phenomena under one objective function
Active inference treats exploration and exploitation as two different ways of tackling the same problem: minimizing uncertainty.claim0.795
§2, comment on expected free energy decomposition.
Active Inference, Curiosity and Insight (Friston et al., 2017)concept0.788
The primary source paper being extracted
Epistemic behavior (exploration) emerges from maximizing mutual information between hidden states and observations.claim0.785
Formal mechanism for curiosity and information-seeking behavior derived from expected free energy.
Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.780
Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.
In active inference, reward can simply be treated as another observation we have a preference over, rather than a special signal.claim0.777
Abstract; central distinction.
Verbal reports, homology, and phylogenetic provenance are insufficient to determine sentience in unconventional agents.claim0.774
Core claim: Turing test and brain homology fail for synthetic, AI, and radically non-human agents; new frameworks required.