Real-time strategy (RTS) is a sub-genre of strategy video games. RTS games are more realistic with dynamic and time-constraint game playing, by abandoning the turn-based rule of its ancestors. Playing with and against computer-controlled players is a pervasive phenomenon in RTS games, due to the convenience and the preference of groups of players. Hence, better game-playing agents are able to enhance game-playing experience by acting as smart opponents or collaborators. One-way of improving game-playing agents' performance, in terms of their economic-expansion and tactical battlefield-arrangement aspects, is to understand the game environment. Traditional commercial RTS game-playing agents address this issue by directly accessing game maps and extracting strategic features. Since human players are unable to access the same information, this is a form of "cheating AI", which has been known to negatively affect player experiences. Thus, we develop a scouting mechanism for RTS game-playing agents, in order to enable game units to explore game environments automatically in a realistic fashion. Our research is grounded in prior robotic exploration work by which we present a hierarchical multi-criterion decision-making (MCDM) strategy to address the incomplete information problem in RTS settings.