Approximation Models of Combat in StarCraft 2

TL;DR

This paper presents a simple, computationally efficient model for predicting battle outcomes in StarCraft 2, aiming to enhance AI combat strategies without heavy micromanagement.

Contribution

The work introduces an approximation model that accurately predicts battle results in RTS games, addressing the computational challenges of complex AI combat simulations.

Findings

Model accurately predicts battle outcomes without micromanagement

Potential for integration into AI to improve strategic balance

Reduces computational complexity of combat simulations

Abstract

Real-time strategy (RTS) games make heavy use of artificial intelligence (AI), especially in the design of computerized opponents. Because of the computational complexity involved in managing all aspects of these games, many AI opponents are designed to optimize only a few areas of playing style. In games like StarCraft 2, a very popular and recently released RTS, most AI strategies revolve around economic and building efficiency: AI opponents try to gather and spend all resources as quickly and effectively as possible while ensuring that no units are idle. The aim of this work was to help address the need for AI combat strategies that are not computationally intensive. Our goal was to produce a computationally efficient model that is accurate at predicting the results of complex battles between diverse armies, including which army will win and how many units will remain. Our results suggest it may be possible to develop a relatively simple approximation model of combat that can accurately predict many battles that do not involve micromanagement. Future designs of AI opponents may be able to incorporate such an approximation model into their decision and planning systems to provide a challenge that is strategically balanced across all aspects of play.