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

## TL;DR

StarAlgo is a C++ library for squad movement planning in StarCraft, utilizing Monte Carlo Tree Search and Negamax algorithms, aiding AI research in complex RTS environments.

## Contribution

It introduces a novel library that integrates MCTS and Negamax algorithms for squad movement planning in StarCraft, facilitating AI development.

## Key findings

- Both algorithms effectively plan squad movements.
- MCTSCD and Negamax show comparable performance.
- The library is easily integrable into existing StarCraft AI bots.

## Abstract

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.