I have been looking into the Monte Carlo Tree Search. However, there seems to be a lack of tutorials or more detail than a wiki walkthru. If anyone has more information I would enjoy reading it.

Thank you.

I recently wrote an AI system for a turn based game called Battle Worlds : Kronos and neither Neural Networks nor building and searching of a game tree in the classical sense had a part in the solution. Neural networks are interesting but hard to control and debug. If the AI behaves in an un-intended way you can't just step into the code and change it because the workings of a NN is opaque even to the developer. Game trees are a nice idea but in their raw form impractical for complex games. Even with chess they hit their limits fast.

My solution builds upon the concept of intelligent agents that use a behavior tree of possible actions. Each unit is controlled by an agent and grouped in squads, squads help units sync their actions. In the scope of one-squad and one-turn it is possible to build a tree of possible actions and search for the optimal path. The strategic behavior of the AI is mostly scripted.

Re neural networks: FFS

Re BTs: most turn-based games are going to have such a complex potential solution space that BTs will break under the weight

You need something that will evaluate your potential actions in a reasonable, yet tunable way... enter utility systems.

enter utility systems.

Any reading suggestions to get a quick overview how utility based systems differ from behaviour trees with "smart" metric-driven transitions?

enter utility systems.

Any reading suggestions to get a quick overview how utility based systems differ from behaviour trees with "smart" metric-driven transitions?

Someone else tell him, please. It's less awkward that way.