Multiobjective exploration of the StarCraft map space

Togelius, Julian; Preuß, Mike; Beume, Nicola; Wessing, Simon; Hagelbäck, Johan; Yannakakis, Georgios N.

doi:10.1109/itw.2010.5593346

Cited by 88 publications

(75 citation statements)

References 15 publications

(20 reference statements)

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“…Focusing on PCG for RTS games, Togelius et al [36,37] presented a multi-objective evolutionary algorithm whose objective was to create maps for this kind of games. Mahlmann et al [26] described a search-based map generator for the game Dune 2, which was able to build playable maps using cellular automata (converting low-resolution matrices into maps fulfilling gameplay requirements).…”

Section: Procedural Content Generationmentioning

confidence: 99%

Procedural Content Generation for Real-Time Strategy Games

Lara-Cabrera¹,

Nogueira-Collazo²,

Cotta³

et al. 2015

IJIMAI

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-Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of realtime strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI.

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Section: Procedural Content Generationmentioning

confidence: 99%

Procedural Content Generation for Real-Time Strategy Games

Lara-Cabrera¹,

Nogueira-Collazo²,

Cotta³

et al. 2015

IJIMAI

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show abstract

“…For example, Julian Togelius et al, designed a SBPCG multiobjective evolutionary algorithm whose objective was create maps for realtime strategy [14,15] and racing [16] games. In a similar way, Ferreira and Toledo [17] presented a SBPCG approach for generating levels for the physicsbased videogame Angry Birds.…”

Section: Introductionmentioning

confidence: 99%

A Spatially-Structured PCG Method for Content Diversity in a Physics-Based Simulation Game

Lara-Cabrera

Alcoba

Fernández-Leiva

2016

Applications of Evolutionary Computation

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Abstract. This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of different levels of difficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the nbody problem, a classical problem in the field of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the difficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with different difficulty in Gravityvolve!.

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“…PCG systems tend to work in isolation, often as a supplement to a human-designed system, designing aspects of the game's world [1][2][3][4]; generating items or abilities suited to the individual currently playing [5,6]; or generating quests or tasks for the player to undertake [7][8][9]. However, automating design as a whole -that is, the design of a game solely by a system and without direct human judgement -remains largely uninvestigated.…”

Section: Introductionmentioning

confidence: 99%

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

Cook

Colton

Gow

2012

Applications of Evolutionary Computation

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Abstract. We present initial results from ACCME, A Co-operative Co-evolutionary Metroidvania Engine, which uses co-operative co-evolution to automatically evolve simple platform games. We describe the system in detail and justify the use of co-operative co-evolution. We then address two fundamental questions about the use of this method in automated game design, both in terms of its ability to maximise fitness functions, and whether our choice of fitness function produces scores which correlate with player preference in the resulting games.

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Multiobjective exploration of the StarCraft map space

Cited by 88 publications

References 15 publications

Procedural Content Generation for Real-Time Strategy Games

Procedural Content Generation for Real-Time Strategy Games

A Spatially-Structured PCG Method for Content Diversity in a Physics-Based Simulation Game

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

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