Distributed Artificial Intelligence

A primer on Multi-Agent Systems, Agent-Based Modeling, and Swarm Intelligence

Francesco Corea
14 min readMar 28, 2019


I. Setting the stage

Almost two years ago, I paused thinking about the future of AI and drew down some “predictions” about where I thought the field was going.

One of those forecasts concerned reaching a general intelligence in several years, not through a super powerful 100-layers deep learning algorithm, but rather through something called collective intelligence. However, except for very obvious applications (e.g., drones), I have not read or seen any big development in the field and I thus thought to dig a bit into that to check what is currently going on.

As part of the AI Knowledge Map then, I will have a look here not only at Swarm Intelligence (SI) but more generally at Distributed AI, which also includes Agent-Based Modeling (ABM) and Multi-Agent Systems (MAS).

II. Distributed AI (DAI)

Image Credit: aurielaki/Shutterstock

Let’s start from the broader classification. Distributed Artificial Intelligence (DAI) is a class of technologies and methods that span from swarm intelligence to multi-agent technologies and that basically concerns the development of distributed solutions for a specific problem.

It can mainly be used for learning, reasoning, and planning, and it is one of the subsets of AI where simulation has a way greater importance than point-prediction. In this class of systems, autonomous learning processing agents (distributed at large scale and independent) reach conclusions or a semi-equilibrium through interaction and communication (even asynchronously). One of the big benefits of those with respect to neural networks is that they do not require the same amount of data to work — far to say though these are simple systems.



Francesco Corea

Data science @ Greycroft. Previously @Balderton @Anthemis @UCLA. All opinions are my own.

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