Initial Results from Co-operative Co-evolution for Automated Platformer Design

Cook, Michael; Colton, Simon; Gow, Jeremy

doi:10.1007/978-3-642-29178-4_20

Cited by 23 publications

(24 citation statements)

References 13 publications

(19 reference statements)

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“…In 2012, Cook et al [19] introduced the first study to use coevolution to evolve game contents. Their focus was on how to make the procedural content generation task more of a shared creative task between a human designer and the proposed automated designing system.…”

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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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: 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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“…By retaining the parents of each generation, we give the system a chance to recover from drops in fitness, which gives the species more time to develop traits that co-operate with the other species. The second version of ANGELINA [11] demonstrates that CCE-based approaches to game design can be expanded to different genres of game. The system's domain is a subgenre of platform games colloquially referred to as Metroidvania.…”

Section: Internal Cce Systemmentioning

confidence: 99%

“…In sections IV and V we describe two iterations of the software, including details of the evolutionary system and additional processes at work within the software. Some of the work described in these sections have previously been described in [9] and [11] respectively. In section VI we give descriptions of several evaluative experiments conducted on versions of ANGELINA, and discuss the results and their meaning for future evaluations of autonomous game design systems.…”

mentioning

confidence: 99%

The ANGELINA Videogame Design System—Part I

Cook

Colton

Gow

2017

IEEE Trans. Comput. Intell. AI Games

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Abstract-Automatically generating content for videogames has long been a staple of game development and the focus of much successful research. Such forays into content generation usually concern themselves with producing a specific game component, such as a level design. This has proven a rich and challenging area of research, but in focusing on creating separate parts of a larger game, we miss out on the most challenging and interesting aspects of game development. By expanding our scope to the automated design of entire games, we can investigate the relationship between the different creative tasks undertaken in game development, tackle the higher-level creative challenges of game design, and ultimately build systems capable of much greater novelty, surprise and quality in their output.This paper, the first in a series of two, describes two case studies in automating game design, proposing cooperative coevolution as a useful technique to use within systems that automate this process. We show how this technique allows essentially separate content generators to produce content that complements each other. We also describe systems that have used this to design games with subtle emergent effects. After introducing the technique and its technical basis in this paper, in the second paper in the series we discuss higher level issues in automated game design, such as potential overlap with computational creativity and the issue of evaluation.

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“…Note that this section does not mention other forms of coevolution that have also been applied in games because they are not directly related with competition (e.g., Cook et al 2012 focused on cooperation and not on competition).…”

Section: (Competitive) Coevolution In Gamesmentioning

confidence: 99%

Virtual player design using self-learning via competitive coevolutionary algorithms

2014

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The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.

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Initial Results from Co-operative Co-evolution for Automated Platformer Design

Cited by 23 publications

References 13 publications

A Systematic Review of Coevolution in Real-Time Strategy Games

A Systematic Review of Coevolution in Real-Time Strategy Games

The ANGELINA Videogame Design System—Part I

Virtual player design using self-learning via competitive coevolutionary algorithms

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