Modern software systems are becoming more and more socio-technical systems composed of distributed and heterogeneous agents from a mixture of people, their environment, and software components. These systems operate under continuous perturbations due to the unpredicted behaviors of people and the occurrence of exogenous changes in the environment. In this article, we introduce a notion of ensembles for which, systems with collective adaptability can be built as an emergent aggregation of autonomous and self-adaptive agents. Building upon this notion of ensemble, we present a distributed adaptation approach for systems composed by ensembles: collections of agents with their respective roles and goals. In these systems, adaptation is triggered by the run-time occurrence of an extraordinary circumstance, called issue. It is handled by an issue resolution process that involves agents affected by the issue to collaboratively adapt with minimal impact on their own preferences. Central to our approach is the implementation of a collective adaptation engine (CAE) able to solve issues in a collective fashion. The approach is instantiated in the context of a smart mobility scenario through which its main features are illustrated. To demonstrate the approach in action and evaluate it, we exploit the DeMOCAS framework, simulating the operation of an urban mobility scenario. We have executed a set of experiments with the goal to show how the CAE performs in terms of feasibility and scalability. With this approach, we are able to demonstrate how collective adaptation opens up new possibilities for tackling urban mobility challenges making it more sustainable respect to selfish and competitive behaviours.

中文翻译：

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通过多智能体集成的集体适应

现代软件系统正成为越来越多的社会技术系统，由来自人、他们的环境和软件组件的混合的分布式和异构代理组成。由于人们的不可预测的行为和环境中外生变化的发生，这些系统在持续的扰动下运行。在本文中，我们介绍了集成的概念，具有集体适应性的系统可以构建为自主和自适应代理的紧急聚合。基于这种集成的概念，我们为由集成组成的系统提出了一种分布式适应方法：具有各自角色和目标的代理集合。在这些系统中，适应是由运行时发生的异常情况触发的，称为问题。它由一个问题解决过程来处理，该过程涉及受问题影响的代理在对他们自己的偏好的影响最小的情况下进行协作。我们方法的核心是实施能够以集体方式解决问题的集体适应引擎 (CAE)。该方法是在智能移动场景的上下文中实例化的，通过该场景来说明其主要特征。为了演示该方法并对其进行评估，我们利用 DeMOCAS 框架，模拟城市交通场景的运行。我们已经执行了一组实验，目的是展示 CAE 在可行性和可扩展性方面的表现。采用这种方法，