Today's distributed and pervasive computing addresses large-scale
cyber-physical ecosystems, characterised by dense and large networks of devices
capable of computation, communication and interaction with the environment and
people. While most research focusses on treating these systems as "composites"
(i.e., heterogeneous functional complexes), recent developments in fields such
as self-organising systems and swarm robotics have opened up a complementary
perspective: treating systems as "collectives" (i.e., uniform, collaborative,
and self-organising groups of entities). This article explores the motivations,
state of the art, and implications of this "collective computing paradigm" in
software engineering, discusses its peculiar challenges, and outlines a path
for future research, touching on aspects such as macroprogramming, collective
intelligence, self-adaptive middleware, learning, synthesis, and
experimentation of collective behaviour.

今天的分布式和普适计算涉及到大规模的网络生态系统，这些系统由密集和大型设备网络组成，这些设备可以进行计算、通信和与环境及人进行交互。本文探讨了在软件工程中这种 “集体计算范式” 的动机、现状和影响，讨论了其特殊的挑战，并概述了未来研究的路径，涉及到宏观编程、集体智能、自适应中间件、学习、合成和集体行为的实验。

面向集体网络物理系统的软件工程

Software Engineering for Collective Cyber-Physical Ecosystems

In many self-organising systems the ability to extract necessary resources
from the external environment is essential to the system's growth and survival.
Examples include the extraction of sunlight and nutrients in organic plants, of
monetary income in business organisations and of mobile robots in swarm
intelligence actions. When operating within competitive, ever-changing
environments, such systems must distribute their internal assets wisely so as
to improve and adapt their ability to extract available resources. As the
system size increases, the asset-distribution process often gets organised
around a multi-scale control topology. This topology may be static (fixed) or
dynamic (enabling growth and structural adaptation) depending on the system's
internal constraints and adaptive mechanisms. In this paper, we expand on a
plant-inspired asset-distribution model and introduce a more general
multi-scale model applicable across a wider range of natural and artificial
system domains. We study the impact that the topology of the multi-scale
control process has upon the system's ability to self-adapt asset distribution
when resource availability changes within the environment. Results show how
different topological characteristics and different competition levels between
system branches impact overall system profitability, adaptation delays and
disturbances when environmental changes occur. These findings provide a basis
for system designers to select the most suitable topology and configuration for
their particular application and execution environment.

在此研究中，我们扩展了一种植物灵感的资产分配模型，引入了一个更普遍的多尺度模型，适用于更广泛的自然和人工系统领域，研究了多尺度控制流程拓扑对系统自适应资产分配能力的影响，结果表明不同的拓扑特征和系统分支之间的不同竞争水平对整个系统的盈利能力、适应延迟和环境变化时的干扰产生影响。这些发现为系统设计人员选择最适合其特定应用和执行环境的拓扑和配置提供了基础。

多尺度动态环境资产分配模型

Multi-Scale Asset Distribution Model for Dynamic Environments

Recent characterisations of self-organising systems depend upon the presence
of a Markov blanket: a statistical boundary that mediates the interactions
between what is inside of and outside of a system. We leverage this idea to
provide an analysis of partitions in neuronal systems. This is applicable to
brain architectures at multiple scales, enabling partitions into single
neurons, brain regions, and brain-wide networks. This treatment is based upon
the canonical micro-circuitry used in empirical studies of effective
connectivity, so as to speak directly to practical applications. This depends
upon the dynamic coupling between functional units, whose form recapitulates
that of a Markov blanket at each level. The nuance afforded by partitioning
neural systems in this way highlights certain limitations of modular
perspectives of brain function that only consider a single level of
description.

基于马尔可夫毯概念，对神经元系统的分区进行分析，凸显模块化认知的限制。