Electromechanical systems manage physical processes through a network of
inter-connected components. Today, programming the interactions required for
coordinating these components is largely a manual process. This process is
time-consuming and requires manual adaptation when system features change. To
overcome this issue, we use autonomous software agents that process semantic
descriptions of the system to determine coordination requirements and
constraints; on this basis, they then interact with one another to control the
system in a decentralized and coordinated manner.Our core insight is that
coordination requirements between individual components are, ultimately,
largely due to underlying physical interdependencies between the components,
which can be (and, in many cases, already are) semantically modeled in
automation projects. Agents then use hypermedia to discover, at run time, the
plans and protocols required for enacting the coordination. A key novelty of
our approach is the use of hypermedia-driven interaction: it reduces coupling
in the system and enables its run-time adaptation as features change.

使用自主软件代理来处理系统的语义描述，以确定协调要求和约束，并以分散协调的方式控制系统，通过超媒体发现运行时所需的计划和协议，以减少系统耦合并在特征变化时实现运行时自适应。

面向工业自动化的自适应协调超媒体环境

Towards Hypermedia Environments for Adaptive Coordination in Industrial  Automation

Multi-agent systems are characterized by environmental uncertainty, varying
policies of agents, and partial observability, which result in significant
risks. In the context of Multi-Agent Reinforcement Learning (MARL), learning
coordinated and decentralized policies that are sensitive to risk is
challenging. To formulate the coordination requirements in risk-sensitive MARL,
we introduce the Risk-sensitive Individual-Global-Max (RIGM) principle as a
generalization of the Individual-Global-Max (IGM) and Distributional IGM (DIGM)
principles. This principle requires that the collection of risk-sensitive
action selections of each agent should be equivalent to the risk-sensitive
action selection of the central policy. Current MARL value factorization
methods do not satisfy the RIGM principle for common risk metrics such as the
Value at Risk (VaR) metric or distorted risk measurements. Therefore, we
propose RiskQ to address this limitation, which models the joint return
distribution by modeling quantiles of it as weighted quantile mixtures of
per-agent return distribution utilities. RiskQ satisfies the RIGM principle for
the VaR and distorted risk metrics. We show that RiskQ can obtain promising
performance through extensive experiments. The source code of RiskQ is
available in this https URL

多主体系统中的风险敏感协调要求和风险测量的多主体强化学习方法。