Evolving robust strategies for an abstract real-time strategy game

Keaveney, David; O’Riordan, Colm

doi:10.1109/cig.2009.5286453

Cited by 11 publications

(12 citation statements)

References 6 publications

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“…It is worth mentioning that two of the games used in the reviewed papers were turn based, rather than actual RTS games. Bellus Bellum Gratia (BBG) was used in [33], [34], and Planet Wars, the Google AI challenge introduced in 2010, was used in [18], [25], [53]. These papers are still considered in this review as the games used here were adjusted and equipped with features that made them closer to RTS games than TBS games, as will be explained later.…”

Section: Finding and Cataloging Sourcesmentioning

confidence: 99%

“…In 2008, Keaveney and O'Riordan [32] developed an RTS game called Bellus Bellum Gratia (BBG), which is a TBS game equipped with some RTS game features including simultaneous turns and imperfect spatial information. A competitive coevolution system was proposed in [33] to handle a BBG game. The authors introduced two measurements to assess both tactical spread and attack coordination among player units.…”

Section: Commentmentioning

confidence: 99%

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A Systematic Review of Coevolution in Real-Time Strategy Games

Elfeky

Elsayed

Marsh³

et al. 2021

IEEE Access

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Real-time strategy (RTS) games are a subgenre of strategy video games. Due to their importance in practical decision-making and digital entertainment over the last two decades, many researchers have explored different algorithmic approaches for controlling agents within RTS games and learning effective strategies and tactics. Among the techniques, coevolutionary algorithms proved to be one of the most popular and successful algorithms for developing such games, in which players can compete or cooperate to achieve the given game's mission. However, as many alternative designs exist with their analysis and the applications reported in diverse publications, a review covering the evolution of such algorithms would be valuable for researchers and practitioners in this domain. This paper aims to provide a systematic review by highlighting why and how coevolution is used in RTS games and analysis of the recent work. The review conducted follows procedural steps to identify, filter, analyse and discuss the existing literature. This structured review articulates the purposes of using coevolution in RTS games and highlights several open questions for future research in this domain.

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Section: Finding and Cataloging Sourcesmentioning

confidence: 99%

Section: Commentmentioning

confidence: 99%

A Systematic Review of Coevolution in Real-Time Strategy Games

Elfeky

Elsayed

Marsh³

et al. 2021

IEEE Access

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“…Avery and Louis did work on coevolving team strategies using influence maps, allowing a group of entities to adapt to opponent moves [11]. Keaveney and Riordan used an abstract RTS game to find players that coordinated their movements with their allies [12]. They coevolved one population of players only on one map, and a second population of players on multiple maps.…”

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)

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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.

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“…Avery and Louis did work on coevolving team strategies using influence maps, allowing a group of entities to adapt to opponent moves [7]. Keaveney and Riordan used an abstract RTS game to coevolve spatial tactical coordination [8]. They coevolved two populations, a population that only played on one map and a population that played on multiple maps.…”

Section: Related Workmentioning

confidence: 99%

Robustness of coevolved strategies in a real-time strategy game

Ballinger

Louis

2013

2013 IEEE Congress on Evolutionary Computation

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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.

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Evolving robust strategies for an abstract real-time strategy game

Cited by 11 publications

References 6 publications

A Systematic Review of Coevolution in Real-Time Strategy Games

A Systematic Review of Coevolution in Real-Time Strategy Games

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

Robustness of coevolved strategies in a real-time strategy game

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