Compute Continuum (CC) systems comprise a vast number of devices distributed
over computational tiers. Evaluating business requirements, i.e., Service Level
Objectives (SLOs), requires collecting data from all those devices; if SLOs are
violated, devices must be reconfigured to ensure correct operation. If done
centrally, this dramatically increases the number of devices and variables that
must be considered, while creating an enormous communication overhead. To
address this, we (1) introduce a causality filter based on Markov blankets (MB)
that limits the number of variables that each device must track, (2) evaluate
SLOs decentralized on a device basis, and (3) infer optimal device
configuration for fulfilling SLOs. We evaluated our methodology by analyzing
video stream transformations and providing device configurations that ensure
the Quality of Service (QoS). The devices thus perceived their environment and
acted accordingly -- a form of decentralized intelligence.

利用基于 Markov blankets 的因果关系过滤器，基于设备的分散评估和推断最优设备配置来实现计算连续系统中的服务质量目标。

设计具有马尔科夫毯的可重构智能系统

Designing Reconfigurable Intelligent Systems with Markov Blankets

This monograph attempts a theory of every 'thing' that can be distinguished
from other things in a statistical sense. The ensuing statistical
independencies, mediated by Markov blankets, speak to a recursive composition
of ensembles (of things) at increasingly higher spatiotemporal scales. This
decomposition provides a description of small things; e.g., quantum mechanics -
via the Schrodinger equation, ensembles of small things - via statistical
mechanics and related fluctuation theorems, through to big things - via
classical mechanics. These descriptions are complemented with a Bayesian
mechanics for autonomous or active things. Although this work provides a
formulation of every thing, its main contribution is to examine the
implications of Markov blankets for self-organisation to nonequilibrium
steady-state. In brief, we recover an information geometry and accompanying
free energy principle that allows one to interpret the internal states of
something as representing or making inferences about its external states. The
ensuing Bayesian mechanics is compatible with quantum, statistical and
classical mechanics and may offer a formal description of lifelike particles.

提出了一种从统计意义上区分事物的理论，着重考察了马尔可夫毛毯对于自组织至非平衡定态的影响，并提供了一种贝叶斯力学用于自主或活动的事物，该理论涵盖量子、统计和经典力学，并可能提供生命粒子的形式化描述。

一个特定物理领域的自由能原理

A free energy principle for a particular physics

We propose a technique for increasing the efficiency of gradient-based
inference and learning in Bayesian networks with multiple layers of continuous
latent vari- ables. We show that, in many cases, it is possible to express such
models in an auxiliary form, where continuous latent variables are
conditionally deterministic given their parents and a set of independent
auxiliary variables. Variables of mod- els in this auxiliary form have much
larger Markov blankets, leading to significant speedups in gradient-based
inference, e.g. rapid mixing Hybrid Monte Carlo and efficient gradient-based
optimization. The relative efficiency is confirmed in ex- periments.

通过表达隐变量的辅助形式，增加贝叶斯网络中多层连续隐变量梯度推断和学习的效率，提高混合蒙特卡罗和梯度优化的速度。