I&#x27;d used minimax before but not heard of Monte Carlo tree search.  It sounds interesting.<p>From [1]:<p>&gt; Although it has been proven that the evaluation of moves in MCTS converges to the minimax evaluation, the basic version of MCTS can converge to it after enormous time. Besides this disadvantage (partially cancelled by the improvements described below), MCTS has some advantages compared to alpha–beta pruning and similar algorithms.
Unlike them, MCTS works without an explicit evaluation function. It is enough to implement game mechanics, i.e. the generating of allowed moves in a given position and the game-end conditions. Thanks to this, MCTS can be applied in games without a developed theory or even in general game playing.<p>&gt; The game tree in MCTS grows asymmetrically: the method concentrates on searching its more promising parts. Thanks to this, it achieves better results than classical algorithms in games with a high branching factor.<p>&gt; Moreover, MCTS can be interrupted at any time, yielding the move it considers the most promising.<p>[1] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Monte_Carlo_tree_search#Advantages_and_disadvantages" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Monte_Carlo_tree_search#Advant...</a>