Artículo publicado en el reciente CoSECiVi 2020, celebrado online el 7 y 8 de octubre de 2020. RESUMEN: The core challenge facing search techniques when used to play Real-Time Strategy (RTS) games is the extensive combinatorial decision space. Several approaches were proposed to alleviate this dimensionality burden, using scripts or action probability distributions, based on expert knowledge. We propose to replace expert-authored scripts by a collection of smaller parametric scripts we call heuristics and use them to pre-select actions for Monte Carlo Tree Search (MCTS). The advantages of this proposal consist of granular control of the decision space and the ability to adapt the agent’s strategy in-game, all by altering the heuristics and their parameters. Experimentation results in μRTS using a proposed implementation have shown a significant performance gain over state-of-the-art agents.