Abstract

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 real- time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI. PURRED on by the emergence of the videogame industry as the main component of the entertainment industry has motivated, research on videogames has acquired increasing notoriety during the last years. Such research spans many areas such as marketing and gamification, psychology and player satisfaction, computational intelligence, education and health (serious games) and computer graphics, just to cite a few. This diversification of research areas is largely motivated by a shift in the priorities of the video game industry: while games used to rely heavily on their graphical quality, other features such as the music, the player immersion into the game and interesting storyline have gained enormous importance. To cope with the plethora of new interesting challenges in the area of

Highlights

  • SPURRED on by the emergence of the videogame industry as the main component of the entertainment industry has motivated, research on videogames has acquired increasing notoriety during the last years

  • As to the topological features, these are extracted from the sphere-of-influence graph (SIG) of each map, which sets a relationship between some set of points based on their spatial arrangement [38]

  • Throughout this paper we have described three case studies that are part of our work in the area of Procedural Content Generation (PCG) for real-time strategy video games

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Summary

INTRODUCTION

SPURRED on by the emergence of the videogame industry as the main component of the entertainment industry has motivated, research on videogames has acquired increasing notoriety during the last years. From the set of genres of videogames, Real-Time Strategy (RTS) games are one of the most exciting sub-genres since they require managing different kind of units and resources in real-time They usually involve the participation of multiple players (not all of them necessarily human) that have to deal with incomplete information during the game; it is precisely this combination of resource management, multiplayer context and partial knowledge of the world what makes them an ideal framework to conduct Artificial Intelligence experiments; many challenging problems, such as resource allocation, adversarial real time planning,. This work deals with the application of PCG techniques in RTS games, firstly by providing a brief review on this issue and, covering specific case studies in which evolutionary search has been employed to produce game components that satisfy certain properties

PROCEDURAL CONTENT GENERATION
CASE STUDIES
Playability-oriented PCG
Introducing Aesthetics
Self-learning of RTS strategies
CONCLUSION
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