Call for AI Research in RTS Games

This position paper discusses AI challenges in the area of real–time strategy games and presents a research agenda aimed at improving AI performance in these popular multi– player computer games. RTS Games and AI Research Real–time strategy (RTS) games such as Blizzard Entertainment’s Starcraft(tm) and Warcraft(tm) series form a large and growing part of the multi–billion dollar computer games industry. In these games several players fight over resources, which are scattered over a terrain, by first setting up economies, building armies, and ultimately trying to eliminate all enemy units and buildings. The current AI performance in commercial RTS games is poor. The main reasons why the AI performance in RTS games is lagging behind developments in related areas such as classic board games are the following: • RTS games feature hundreds or even thousands of interacting objects, imperfect information, and fast–paced micro–actions. By contrast, World–class game AI systems mostly exist for turn–based perfect information games in which the majority of moves have global consequences and human planning abilities therefore can be outsmarted by mere enumeration. • Video games companies create titles under severe time constraints and do not have the resources and incentive (yet) to engage in AI research. • Multi–player games often do not require World–class AI performance in order to be commercially successful as long as there are enough human players interested in playing the game on–line. • RTS games are complex which means that it is not easy to set up an RTS game infrastructure for conducting AI experiments. Closed commercial RTS game software without AI interfaces does not help, either. The result is a lack of AI competition in this area which in the classic games sector is one of the most important driving forces of AI research. Copyright c © 2004, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. To get a feeling for the vast complexity of RTS games, imagine to play chess on a 512×512 board with hundreds of slow simultaneously moving pieces, player views restricted to small areas around their own pieces, and the ability to gather resources and create new material. While human players sometimes struggle with micro– managing all their objects, it is the incremental nature of the actions that allows them to outperform any existing RTS game AI. The difference to classic abstract games like chess and Othello in this respect is striking: many moves in these games have immediate global effects. This makes it hard for human players to consider deep variations with all their consequences. On the other hand, computers programs conducting full–width searches with selective extensions excel in complex combinatorial situations. A notable exception is the game of go in which — like in RTS games — moves often have only incremental effects and today’s best computer programs are still easily defeated by amateurs (Müller 2002). It is in these domains where the human abilities to abstract, generalize, reason, learn, and plan shine and the current commercial RTS AI systems — which do not reason nor adapt — fail. Other arguments in favor of AI research in RTS games are: • (RTS) games constitute well–defined environments to conduct experiments in and offer straight–forward objective ways of measuring performance, • RTS games can be tailored to focus on specific aspects such as how to win local fights, how to scout effectively, how to build, attack, and defend a town, etc., • Strong game AI will likely make a difference in future commercial games because graphics improvements are beginning to saturate. Furthermore, smarter robot enemies and allies definitely add to the game experience as they are available 24 hours a day and do not get tired. • The current state of RTS game AI is so bad that there are a lot of low–hanging fruits waiting to be picked. Examples include research on smart game interfaces that alleviate human players from tedious tasks such as manually concentrating fire in combat. Game AI can also help in the development of RTS games — for instance by providing tools for unit balancing. • Finally, progress in RTS game AI is also of interest for the military which uses battle simulations in training programs (Herz & Macedonia 2002) and also pursues research into autonomous weapon systems.