Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.

翻译：集体性许多系统（包括自然系统和人工系统）的重要属性。通过利用大量个体，通常能产生远超最聪明个体能力的效应，甚至能从不太聪明的个体中涌现出智能的集体行为。事实上，集体智能——即群体以看似智能的方式共同行动的能力——正日益成为工程化计算系统的设计目标，这一趋势受到物联网、群体机器人学和众包计算等近期技术科学趋势的驱动。多年来，自然与人工系统中观察到的集体智能一直为工程思想、模型和机制提供灵感来源。如今，人工与计算集体智能已被视为公认的研究课题，涵盖多种技术、目标系统类型及应用领域。然而，计算机科学领域内关于该主题的研究图景仍存在大量碎片化现象，多数研究团体和成果的垂直性使得核心基础思想与参考框架难以提取。当前挑战在于识别、整合并最终关联涉及智能集体的不同领域和方法。为弥补这一不足，本文从计算机科学家和工程师的视角出发，通过一系列宏观问题构建集体智能研究图谱。据此，本文涵盖初步概念、基础原理及主要研究视角，为人工与计算集体智能工程领域的研究者指明机遇与挑战。