Comparing coevolution, genetic algorithms, and hill-climbers for finding real-time strategy game plans

Ballinger, Christopher; Louis, Sushil J.

doi:10.1145/2464576.2464604

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…We note there is contextual variability in how suitable each unit in this list is for the task of countering, but this is the exhaustive list of such units. There is also an argument to be made that ignoring a threat is sometimes the best strategy -but we are specifically exploring and measuring anticipation and reaction, not optimal strategy selection as in (Ballinger and Louis 2013;Buro et al 2012;Churchill et al 2011;Ontañón et al 2013). Table 4 in Appendix , details the aforementioned risks across all StarCraft races.…”

Section: Discussionmentioning

confidence: 99%

Risk Management: Anticipating and Reacting in StarCraft

Amos-Binks,

Weber

2023

AIIDE

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Managing risk with imperfect information is something humans do every day, but we have little insight into the abilities of AI agents to do so. We define two risk management strategies and perform an ability-based evaluation using StarCraft agents. Our evaluation shows that nearly all agents mitigate risks after observing them (react), and many prepare for such risks before their appearance (anticipate). For this evaluation, we apply traditional causal effect inference and causal random forest methods to explain agent behavior. The results highlight different risk management strategies among agents, others strategies that are common to agents, and overall encourage evaluating agent risk management abilities in other AI domains.

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Section: Discussionmentioning

confidence: 99%

Risk Management: Anticipating and Reacting in StarCraft

Amos-Binks,

Weber

2023

AIIDE

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“…In RTS games, Ballinger and Louis showed that coevolution led to more robust build orders. Buildorder optimization enables players to generate the right mix and numbers of units meeting a strategic need [28]. Avery and Louis coevolved team-tactics using a set of IMs, navigating a group of friendly units to move and attack enemy units on the basis of the opponent's position [29].…”

Section: Related Workmentioning

confidence: 99%

Co-evolving Real-Time Strategy Game Micro

Adhikari

Louis

Liu

et al. 2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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We investigate competitive co-evolution of unit micromanagement in real-time strategy games. Although good longterm macro-strategy and good short-term unit micromanagement both impact real-time strategy games performance, this paper focuses on generating quality micro. Better micro, for example, can help players win skirmishes and battles even when outnumbered. Prior work has shown that we can evolve micro to beat a given opponent. We remove the need for a good opponent to evolve against by using competitive co-evolution to evolve high-quality micro for both sides from scratch. We first co-evolve micro to control a group of ranged units versus a group of melee units. We then move to co-evolve micro for a group of ranged and melee units versus a group of ranged and melee units. Results show that competitive co-evolution produces good quality micro and when combined with the well-known techniques of fitness sharing, shared sampling, and a hall of fame takes less time to produce better quality micro than simple co-evolution. We believe these results indicate the viability of co-evolutionary approaches for generating good unit micro-management.

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Educational Simulations of the Evolution of Spaceflight

Bainbridge

2018

Computer Simulations of Space Societies

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Comparing coevolution, genetic algorithms, and hill-climbers for finding real-time strategy game plans

Cited by 7 publications

References 3 publications

Risk Management: Anticipating and Reacting in StarCraft

Risk Management: Anticipating and Reacting in StarCraft

Co-evolving Real-Time Strategy Game Micro

Educational Simulations of the Evolution of Spaceflight

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