Right now I am using iterative deepening AB minimax with memory(MTD(f)) + Monte Carlo search as the evaluation function and it is performing better than my earlier minimax algorithm and better than a basic Monte Carlo search.

The algorithm is performing like a Monte Carlo search in the beginning: the search depth is in the range [1-4] (branching factor ~ 122 and the leaf nodes are evaluated using Monte Carlo search which takes time) but since it's storing the node's evaluation in a transposition table, this guides minimax later when the search can go deep towards the end of the game. I am trying to find a good combination of Monte Carlo and minmax especially that the current implementation suffers from the horizon effect and has a very small search depth at the beginning.

I am thinking about the following approaches to improve my player:

- Improve the evaluation function (Alvaro's suggestions sound reasonable) and maybe use quiescence search to mitigate the horizon effect.

- Implement my AI using a purely Monte Carlo search (it's Aheuristic nature might work for this game)