Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: &lt;italic&gt;Planet Wars&lt;/italic&gt;

Nogueira-Collazo, Mariela; Cotta, Carlos; Fernández-Leiva, Antonio J.

doi:10.1109/tciaig.2015.2499281

Cited by 8 publications

(9 citation statements)

References 22 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 2016, Nogueira-Collazo et al [53] proposed a competitive coevolution system with two species: one to evolve players with winning strategies, and the second to evolve game maps that make it harder for the coevolved players to win. They introduced an algorithm with three variants that differed in how they updated HoF members based on measurements that depend on quality and diversity.…”

Section: Commentmentioning

confidence: 99%

“…This work produced victorious strategies over both coevolved and optimised players, which reflect the self-adjustment capacities of the proposed algorithms. Two years later, they extended this work [53] to use coevolution in evolving game contents as explained earlier.…”

Section: Commentmentioning

confidence: 99%

“…[37], [15], [52], [19], [51], [10], [9], [25], [66], [11], [18], [12], [53], [67] [63], [42], [75], [33], [34] GP [44] Neural Networks vs Genetic Programming vs Modular controller [73] game strategies. We do not have an explanation for this; however, maybe the lack of information on the used memory technique hinders the appearance of using this technique for other coevolution purposes.…”

Section: ) Critique Across the Publicationsmentioning

confidence: 99%

“…Exploring the use of cooperative coevolution is a possible way to improve CAs [40], [45], [60], [72]. Further improvement to CAs might be achieved by using a multipopulation approach [25], [30], [40], [53]). Utilising the coevolved plans in predicting opponents' plans was also reported by researchers [9], [11], [33], [52]).…”

Section: Future Workmentioning

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%

Section: Commentmentioning

confidence: 99%

Section: ) Critique Across the Publicationsmentioning

confidence: 99%

Section: Future Workmentioning

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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Adversarial genetic programming for cyber security: a rising application domain where GP matters

O’Reilly¹,

Toutouh²,

Pertierra³

et al. 2020

Genet Program Evolvable Mach

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Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a vital problem domain, arguably occupying a position at the frontier where GP matters. Additionally, it prompts research questions around evolving complex behavior by expressing different abstractions with GP and opportunities to reconnect to the Machine Learning, Artificial Life, Agent-Based Modeling and Cyber Security communities. We present a framework called RIVALS which supports the study of network security arms races. Its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them.

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Toward competitive multi-agents in Polo game based on reinforcement learning

Movahedi

Bastanfard

2021

Multimed Tools Appl

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Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: <italic>Planet Wars</italic>

Cited by 8 publications

References 22 publications

A Systematic Review of Coevolution in Real-Time Strategy Games

A Systematic Review of Coevolution in Real-Time Strategy Games

Adversarial genetic programming for cyber security: a rising application domain where GP matters

Toward competitive multi-agents in Polo game based on reinforcement learning

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