Partially Observable Markov Decision Process (POMDP)

Modeling framework for discrete state-space decision-making under uncertainty, used as generative model in active inference.

Neighborhood — ranked by edge-count

paper

concept

Discrete State-Space Models
associated_with
Mathematical formalism used in active inference for modeling hierarchical and discrete brain processes.

framework

Markov Decision Process (MDP)
related_to
Generative model substrate for active inference; discrete states, actions, outcomes, and temporal policies.
Partially Observed Markov Decision Process
related_to
Alternative approach noted but dismissed as computationally intractable for the rule-learning problem
Active Inference
implements
Foundational framework by Karl Friston; the paper extends it to three hierarchical levels for modeling meta-awareness.

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.

Markov Propertyconcept0.753
Assumption required by IIT 3.0/4.0 and PyPhi; tested for each optimal time series derived from (C)ARR.
Markov Blanketconcept0.724
A statistical partition of states that separates internal states from external hidden states; fundamental to self-organization in the paper.
A Markov blanket is (almost) inevitable in coupled dynamical systems with short-range interactions.claim0.723
Argument that physical laws inevitably produce Markov blankets.
Multiple-choice evaluation method for PM trainingmethod0.717
Using language model log probabilities of answer choices (A)/(B) to produce preference labels.
Communicating Sequential Processes (CSP)framework0.716
A formal language for describing concurrent processes via message passing.
Multi-layer Perceptron (MLP)method0.713
Feed-forward neural network with hidden layers, capable of representing non-linearly separable functions.
Heuristic Search for Optimal Time Series (Markov + Conditional Independence)method0.709
Iterative procedure searching token counts in [50,100,...,1000] to find concatenation of (C)ARR satisfying IIT's Markov and conditional independence assumptions.
The principal Markov blanket identified by spectral graph theory formed a clustered structure with internal subsystems enshrouded by sensory and active subsystems.finding0.706
Visual and quantitative observation of Markov blanket emergence.