RTS games — such as StarCraft by Blizzard Entertainmentand Command and Conquer by Electronic Arts — arepopular video games that can be described as real-time
war simulations in which players delegate units under theircommand to gather resources, build structures, combat and sup-port units, scout opponent locations, and attack. The winner ofan RTS game usually is the player or team that destroys theopponents’ structures first.
Unlike abstract board games like chess and go, moves in RTSgames are executed simultaneously at a rate of at least eightframes per second. In addition, individual moves in RTS gamescan consist of issuing simultaneous orders to hundreds of unitsat any given time. If this wasn’t creating enough complexityalready, RTS game maps are also usually large and states are onlypartially observable, with vision restricted to small areas aroundfriendly units and structures. Complexity by itself, of course, isnot a convincing motivation for studying RTS games and build-ing AI systems for them. What makes them attractive researchsubjects is the fact that, despite the perceived complexity,humans are able to outplay machines by means of spatial andtemporal reasoning, long-range adversarial planning and plan
n In recent years, real-time strategy (RTS)games have gained attention in the AI researchcommunity for their multitude of challengingand relevant real-time decision problems thathave to be solved in order to win against humanexperts or to collaborate effectively with otherplayers in team games. In this report we moti-vate research in this area, give an overview ofpast RTS game AI competitions, and discussfuture directions.
recognition, state inference, and opponent model-ing, which — we hypothesize — are enabled bypowerful hierarchical state and action abstractions.
RTS Game Competitions An established way of improving AI systems forabstract games like chess has been running com-petitions. Applying this idea to commercial RTSgames is tricky, because they usually don’t provideprogramming interfaces. So, in 2002 we set out cre-ating a free software RTS game system — OpenReal-Time Strategy (ORTS),1 which was the plat-form used in four annual game competitions heldfrom 2006 to 2009. There were 4–5 game categoriesfocusing on important subtasks — such as resourcegathering (which is an exercise in multiagent pathfinding) and small-scale combat — and a full-fea-tured RTS game including partial observability. TheORTS competitions mostly attracted academicentries and benefited from the requirement torelease source code.
Since the advent of the Brood War ApplicationProgramming Interface (BWAPI) C++ library in2009, the focus has quickly moved from ORTS toStarCraft. StarCraft by Blizzard Entertainment isone of the most popular commercial RTS games ofall time. BWAPI not only allows programs to inter-act with the game engine directly to playautonomously, but also makes it easy to playgames against strong human players, which is ide-al for measuring progress.2
The first StarCraft AI competition was organizedby Ben Weber at the University of California, San-ta Cruz in 2010 as part of the AAAI Artificial Intel-ligence and Interactive Digital Entertainment(AIIDE) conference program.3 There were fourgame categories, which ranged from skirmish tothe full StarCraft game. There were 17 entries forthe full game and 34 entries overall in all cate-gories.
One year later, the second AIIDE StarCraft AIcompetition was held at the University of Alberta.An international field of 18 entries from seven dif-ferent countries entered the competition, consist-ing of 13 academic and 5 independent teams.Team sizes varied from a single person to an entireAI class from Berkeley. Using newly developedtournament software, 2,360 full games were playeddistributed over 20 lab machines during four daysin August. Two years in a row, the authors of thethree best programs in the AIIDE competitionsreceived StarCraft 2 collector’s edition signed bythe StarCraft team — a valuable prize donated byBlizzard Entertainment. To speed up developmentfor future competitions, releasing the source codewas made mandatory. Since then, a couple moreannual StarCraft AI competitions have been initi-ated, such as CIG 2011 and SSCAI 2011. A com-
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plete competition list is available at the BWAPIwebsite.4
State of the Art RTS Game AI Systems
The current state of the art in StarCraft bots com-prises a mix of AI algorithms and scripted strate-gies. Because StarCraft is such a complex game,most bots are decomposed into components corre-sponding to strategic elements of the game, such aseconomic planning, high-level decision making,and unit micromanagement. Due to the high com-plexity of these strategic elements, most botsimplement these components as a series of script-ed rules based on observations from expert-levelhuman play. However, over the past two years wehave seen more intelligent AI solutions tacklethese problems.
Each of the top-performing bots seems to haveits own specialty AI solutions to one or more ofthese elements. For instance, the 2010 winner,Berkeley’s Overmind (Huang 2011), used potentialfields to guide the attack formations of its flyingunits, making their attacks much more effective.Skynet, the 2011 winner, used path finding toguide its units to safety to avoid being surrounded.UAlbertaBot, the 2011 second place finisher, useda heuristic search algorithm to plan its economicbuild orders (Churchill and Buro 2011), resultingin more efficient unit production. Other botsimplemented particle models for state estimation(Weber, Mateas, and Jhala 2011), plan recognition(Synnaeve and Bessire 2011a), and Bayesian mod-els for unit control (Synnaeve and Bessire 2011b).The source code of the 2011 competition entriescan be downloaded from the competition websiteto serve as a starting point for subsequent compe-titions.
AI solutions have a large advantage over hard-coded scripts due to their ability to adapt tochanges in the environment or opponent strategy.However, the most important aspect of any RTSgame — high-level strategy decisions — still evadesAI solutions. The 2011 Man Versus Machine Matchsaw former StarCraft pro Oriol Vinyals easily defeatSkynet, the winner of the computer competition.Despite strong opening play by Skynet, Vinyalswas able to exploit the bot’s inability to adapt tohis midgame strategy changes. “Overall,” com-mented Vinyals after the match, “Skynet had goodmacro, generally good micro, but bad strategicdecisions and [lack of scouting/prediction].” Figure1 shows crucial game situations Skynet mishan-dled.
2012 RTS Game AI Competitions When humans play a game, they often take time
to study their opponents to learn their strategy,strengths, and weaknesses. Up until now, StarCraftcompetitions have not allowed bots to retain datafrom previously played games. In 2011 we sawmany early aggression bots that were able to defeattheir opponents in the same way every game. The2012 AIIDE StarCraft AI competition, which washeld in August 2012 again at the University ofAlberta, allowed bots to store data about previousmatches, thereby forcing bots to implement awider variety of strategies. Also, until now softwarelimitations did not allow bots to choose their raceat random, which was fixed for the 2012 competi-tion, adding an additional strategic element to thegame. The results of the 2012 AIIDE StarCraft AIcompetition will be presented at the AIIDE confer-ence in October, which also will host a workshopon RTS game AI. The CIG 2012 competition will beheld in September, and the competition at SSCAI2012 is still in the planning stage.
Notes1. The ORTS website is located at www.cs.ualberta.ca/˜mburo/orts.
2. The C++ StarCraft BroodWar Interface (BWAPI) can befound at code.google.com/p/bwapi.
3. See AIIDE StarCraft AI Competition. The URLS areeis.ucsc.edu/StarCraftAICompetition (for 2010), www.cs.ualberta.ca/~mburo/sc2011 (for 2011) and starcraftaicompetition.com (for 2012). The Twitter account can befound at twitter.com/StarCraftAIComp.
4. See code.google.com/p/bwapi/wiki/Competitions.
References Churchill, D., and Buro, M. 2011. Build Order Optimiza-tion in StarCraft. In Proceedings, The Seventh AAAI Confer-ence on Artificial Intelligence and Interactive Digital Enter-tainment, 14–19. Menlo Park, CA: AAAI Press.
Huang, H. 2011. Skynet Meets the Swarm: How theBerkeley Over-Mind Won the 2010 StarCraft AI Compe-tition. Ars Technica (18 January).
Synnaeve, G., and Bessire, P. 2011a. A Bayesian Model forPlan Recognition in RTS Games Applied to StarCraft. InProceedings, The Seventh AAAI Conference on Artificial Intel-ligence and Interactive Digital Entertainment. Menlo Park,CA: AAAI Press.
Synnaeve, G., and Bessire, P. 2011b. A Bayesian Model forRTS Unit Control Applied to StarCraft. In Proceedings ofthe 2011 IEEE Conference on Computational Intelligence andGames, 190–196. Piscataway, NJ: Institute of Electricaland Electronics Engineers.
Weber, B. G.; Mateas, M.; and Jhala, A. 2011. A ParticleModel for State Estimation in Real-Time Strategy Games.In Proceedings, The Seventh AAAI Conference on ArtificialIntelligence and Interactive Digital Entertainment, 103–108.Menlo Park, CA: AAAI Press.
Michael Buro is an associate professor in the ComputingScience Department at the Unversity of Alberta.
David Churchill is a PhD student in the Computing Sci-ence Department at the University of Alberta.
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Figure 1. 2011 AIIDE Man Versus Machine Competition — Oriol VinyalsVersus Competition Winner Skynet.
Despite Skynet’s strong openings a lack of adaptation and poor attack target-ing cost it the match. In (a) Skynet playing as Protoss (bottom left) attemptsan early attack on the human’s base, which is defended by siege tanks. The botdoesn’t realize that it can’t succeed. In the final battle of the match (shown inb), Skynet focused on attacking Vinyals’s medics, rather than attacking thetanks, allowing them to push through the defenses.
AI Magazine invites organizers of AI competi-tions to submit short reports summarizing theobjectives, rules and outcomes of recently held
events. We also invite competition updatesfrom organizers that previously published inthese pages, documenting results of morerecently held events. Please contact Sven
