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Real-Time Strategy (RTS) games have recently become a popular testbed forartificial intelligence research. They represent a complex adversarial domainproviding a number of interesting AI challenges. There exists a wide variety ofresearch-supporting software tools, libraries and frameworks for one RTS gamein particular -- StarCraft: Brood War. These tools are designed to addressvarious specific sub-problems, such as resource allocation or opponentmodelling so that researchers can focus exclusively on the tasks relevant tothem. We present one such tool -- a library called StarAlgo that produces plansfor the coordinated movement of squads (groups of combat units) within the gameworld. StarAlgo library can solve the squad movement planning problem using oneof two algorithms: Monte Carlo Tree Search Considering Durations (MCTSCD) and aslightly modified version of Negamax. We evaluate both the algorithms, comparethem, and demonstrate their usage. The library is implemented as a static C++library that can be easily plugged into most StarCraft AI bots.

StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and Negamax