Partially Observed Markov Decision Process

Alternative approach noted but dismissed as computationally intractable for the rule-learning problem

Neighborhood — ranked by edge-count

framework

Partially Observable Markov Decision Process (POMDP)
related_to
Modeling framework for discrete state-space decision-making under uncertainty, used as generative model in active inference.

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 Decision Process (MDP)framework0.820
Generative model substrate for active inference; discrete states, actions, outcomes, and temporal policies.
Markov Propertyconcept0.732
Assumption required by IIT 3.0/4.0 and PyPhi; tested for each optimal time series derived from (C)ARR.
Perception-Action Cycleconcept0.718
Circular causality between perception and action; central to enactive interpretation
partial involutionsconcept0.718
Fixed-point free partial injective functions used as simple reversible dynamical processes in Geometry of Interaction.
Multiple-choice evaluation method for PM trainingmethod0.716
Using language model log probabilities of answer choices (A)/(B) to produce preference labels.
Markov Blanketconcept0.713
A statistical partition of states that separates internal states from external hidden states; fundamental to self-organization in the paper.
We evaluate the relative life of a process by making predictions based on thought-experiments or simulations.claim0.710
Hidden Markov Modelmethod0.710
Core computational method used to infer pain-belief from online observations of happiness