Just returned from a lovely stay in Chania, Crete for the Advanced Course on Artificial Intelligence, a yearly summer school by EurAI. Besides the beautiful scenery and lovely people, there were lots of interesting talks to enjoy. Most talks stuck to the theme ‘AI for multi-agent worlds’ quite well.
Although I learned a lot in general, there were some key take-aways for me:
- virtually any setting has some multi-agent aspect to it, but it might not be worthwile to actually take it into account
- when going from a single- to a multi-agent setting, typically requires engineering and analysis of the resulting system from a game-theoretic point of view. Lots of the presented work had some deep connection with game theory, which is quite exciting in my point of view.
- going from single- to a multi-agent setting – unfortunately – typically also results in problem spaces that are much harder: they are typically intractable, multimodal and sometimes even unstable. Typical solutions to get out involve leveraging some feature of the problem domain or resorting to approximation.
- evaluation in the multi-agent domain may be quite challenging. Take the adversarial setting of learning to play a game. Here, a version \(n\) of an agent may beat version \(n-1\) but be beaten by version \(n-2\).
Overall, the summer school was well organized and was happy to meet many awesome people working on interesting problems in the AI space.