# Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

arXiv: https://arxiv.org/abs/2304.05147 · HTML: https://mlsanity.com/paper/2304.05147/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 250 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 25 of 25)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Programmatically Interpretable Reinforcement Learning](https://mlsanity.com/paper/1804.02477/) (2018)
- [A Survey on Distributed Machine Learning](https://mlsanity.com/paper/1912.09789/) (2019)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence](https://mlsanity.com/paper/1712.00600/) (2017)
- [Collective Intelligence for Deep Learning: A Survey of Recent Developments](https://mlsanity.com/paper/2111.14377/) (2021)
- [Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey](https://mlsanity.com/paper/2203.14009/) (2022)
- [Embodied Evolution in Collective Robotics: A Review](https://mlsanity.com/paper/1709.08992/) (2017)
- [Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition](https://mlsanity.com/paper/1709.05412/) (2017)
- [Self-Organization and Artificial Life](https://mlsanity.com/paper/1903.07456/) (2019)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Proof of Witness Presence: Blockchain Consensus for Augmented Democracy in Smart Cities](https://mlsanity.com/paper/1907.00498/) (2019)
- [Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling](https://mlsanity.com/paper/2201.03473/) (2022)
- [Evolution of Swarm Robotics Systems with Novelty Search](https://mlsanity.com/paper/1304.3362/) (2013)
- [Bat Algorithm: Literature Review and Applications](https://mlsanity.com/paper/1308.3900/) (2013)
- [Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy](https://mlsanity.com/paper/1803.05464/) (2018)
- [Braitenberg Vehicles as Developmental Neurosimulation](https://mlsanity.com/paper/2003.07689/) (2020)
- [Learning Robot Swarm Tactics over Complex Adversarial Environments](https://mlsanity.com/paper/2109.05663/) (2021)
- [From Participatory Sensing to Mobile Crowd Sensing](https://mlsanity.com/paper/1401.3090/) (2014)
- [An Overview on Optimal Flocking](https://mlsanity.com/paper/2009.14279/) (2020)
- [A comprehensive review of firefly algorithms](https://mlsanity.com/paper/1312.6609/) (2013)
- [Collective Learning](https://mlsanity.com/paper/1912.02580/) (2019)
- [Frontiers in Collective Intelligence: A Workshop Report](https://mlsanity.com/paper/2112.06864/) (2021)

## Built on by (most cited first, 3 of 3)

- [The Era of Agentic Organization: Learning to Organize with Language Models](https://mlsanity.com/paper/2510.26658/) (2025)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Programming Distributed Collective Processes in the eXchange Calculus](https://mlsanity.com/paper/2401.11212/) (2024)

## Abstract (arXiv)

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.