Monte Carlo tree search can use either light or heavy playouts. Light playouts consist of random moves while heavy playouts apply various heuristics to influence the choice of moves.Świechowski, M.; Mańdziuk, J., "Self-Adaptation of Playing Strategies in General Game Playing" (2010), IEEE Transactions on Computational Intelligence and AI in Games, vol: 6(4), pp. 367-381, , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6571225&isnumber=4804729 These heuristics may employ the results of previous playouts (e.g. the Last Good Reply heuristic) or expert knowledge of a given game. For instance, in many Go-playing programs certain stone patterns in a portion of the board influence the probability of moving into that area. Paradoxically, playing suboptimally in simulations sometimes makes a Monte Carlo tree search program play stronger overall.
