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