SCAIL: An integrated Starcraft AI system

Young, J. M.; Smith, Fran; Atkinson, Christopher; Poyner, Ken; Chothia, Tom

doi:10.1109/cig.2012.6374188

Cited by 13 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For space reasons, we do not include an exhaustive comparison of the architectures of all the participating bots. Some more bots than the ones described here have been documented by their authors, such as SCAIL [11] or QUORUM [10]. Let us now focus on their performance in recent competitions.…”

mentioning

confidence: 85%

StarCraft Bots and Competitions

Churchill

Preuß

Richoux

et al. 2016

Encyclopedia of Computer Graphics and Games

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 85%

StarCraft Bots and Competitions

Churchill

Preuß

Richoux

et al. 2016

Encyclopedia of Computer Graphics and Games

View full text Add to dashboard Cite

show abstract

“…Chris Miles' dissertation work used coevolution to produce influence maps that allowed RTS AI players to use complex and challenging strategies [9]. In addition to coevolutionary methods, other online and offline methods have been used to find good RTS strategies, such as case-based reasoning, genetic algorithms, dynamic scripting, and reinforcement learning [10], [11], [12], [13], [14]. Research not only focus' on producing a player to manage an entire RTS game, but also on producing strategies for specific elements of play, such as combat, positioning, navigation, cooperation and resource management [15], [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

Robustness of coevolved strategies in a real-time strategy game

Ballinger

Louis

2013

2013 IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

This paper evaluates the performance of real-time strategy game strategies produced by coevolution. Specifically, we evaluate the robustness of coevolution solutions by having them compete against solutions produced by a genetic algorithm, three hand-tuned baselines, and a human opponent. Our earlier work has shown that genetic algorithms routinely find optimal solutions for defeating the opponents used in training. In this paper, our results show that coevolution finds strategies that defeat genetic algorithm strategies and two of our baselines, without seeing any of the baselines or genetic algorithm solutions during evaluation. A human player who competed against these strategies found that the coevolutionary strategy was the most challenging to defeat, but could be easily defeated using strategies not previously encountered by coevolution. This work informs our research on improving the robustness of real-time strategy players through coevolutionary approaches.

show abstract

“…In addition to coevolutionary methods, researchers have used other online and offline methods to find good RTS strategies, such as case-based reasoning, genetic algorithms, dynamic scripting, and reinforcement learning [13], [14], [15], [16], [17]. While some research focuses on producing a player to manage an entire RTS game, other research focuses on finding good strategies for specific RTS game elements, such as combat, positioning, navigation, cooperation and resource management [18], [19], [20], [21].…”

Section: Related Workmentioning

confidence: 99%

Finding robust strategies to defeat specific opponents using case-injected coevolution

Ballinger

Louis

2013

2013 IEEE Conference on Computational Inteligence in Games (CIG)

View full text Add to dashboard Cite

Abstract-Finding robust solutions that are also capable of beating specific opponents presents a challenging problem. This paper investigates solving this problem by using case-injection with a coevolutionary algorithm. Specifically, we recorded winning strategies used by a human player against a coevolved strategy and then injected the player's strategies into the coevolutionary teachset. We compare the strategies produced by caseinjected coevolution to strategies produced by a genetic algorithm that only evaluated against the player's strategies. In this paper, our results show that genetic algorithms do not work well against sufficiently difficult opponents. However, coevolution eventually learns to defeat these opponents by first bootstrapping strategies that work well in general, which drives the population closer to strategies that can defeat the challenging opponent. This work informs our research on finding robust real-time strategy game players that also defeat specific opponents.

show abstract

SCAIL: An integrated Starcraft AI system

Cited by 13 publications

References 9 publications

StarCraft Bots and Competitions

StarCraft Bots and Competitions

Robustness of coevolved strategies in a real-time strategy game

Finding robust strategies to defeat specific opponents using case-injected coevolution

Contact Info

Product

Resources

About