The paper “Reactive Planning Idioms for Multi-Scale Game AI” (Weber et al.), published last year in the proceedings of the IEEE Conference on Computation Intelligence and Games (CIG 2010), proposes a technique called reactive planning for designing a bot for Real-time strategy games (RTS). The agent is implemented in ABL (a behavioral language), an environment that allows the programmer to embed the multi-level reasoning that is required for efficient and complex RTS bots. A bot for RTS games (such as the StarCraft) must deal simultaneously with several goals, making intelligent high-level decisions while micromanaging units in combat, and ABL provides features such as daemons, messaging (memory), managers and micromanagement behaviors that can be of great help for such task. The authors propose a specific framework, for the structure of the bot and interfaces, and demonstrate that the resulting agent is able to beat the built-in StarCraft bot. However, when tested against moderately skilled human players, the agent performs poorly. As far as we understood, this work deals mainly with traditional Artificial Intelligence. The open question now is: can we model some kind of adaptive behavior in this ABL environment?