It was around 2010 before all the reinforcement learning madness :)

It was a university student competition in (mostly computer science bachelor and master students).

The core algorithm is https://en.wikipedia.org/wiki/Expectiminimax which is a type of heuristic tree search over the possible board states.

Winning such a tournament meant you can write efficient C++ code (so you can search more states during the allocated turn) and you can invent/feature-engineer good heuristics that estimate how close is a board state to winning the game.

I don't remember what heuristics was used, I don't even remember the rules :)

But I've uploaded the source to github a few years ago:

https://github.com/amitport/senet-ai

The heuristics are at: https://github.com/amitport/senet-ai/blob/master/SmartUtilit...

I can't really make sense of it anymore :) but it seems to work

(Fixed link: https://github.com/amitport/senet-ai/blob/master/SmartUtilit... )

Thanks! I think these days the idea is to replace hand-crafted heuristics with (deep) reinforcement learning. But adversarial search by minimax and friends is still the core of AI players, far as I can tell.