Abstract-The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and non-digital (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centering on what kind of content is generated, how the content is represented and how the quality/fitness of the content is evaluated; searchbased procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.

Abstract-Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design. Personalization of user experience via affective and cognitive modeling, coupled with real-time adjustment of the content according to user needs and preferences are important steps towards effective and meaningful PCG. Games, Web 2.0, interface and software design are amongst the most popular applications of automated content generation. The paper provides a taxonomy of PCG algorithms and introduces a framework for PCG driven by computational models of user experience. This approach, which we call Experience-Driven Procedural Content Generation (EDPCG), is generic and applicable to various subareas of HCI. We employ games as an indicative example of rich HCI and complex affect elicitation, and demonstrate the approach's effectiveness via dissimilar successful studies.

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the generated content. Multiple PCGML methods are covered, including neural networks: long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models: n-grams and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.

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Abstract-Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human player. This requires a way to create accurate models of players' driving styles, as well as a tentative definition of when a racing track is fun, both of which are provided. We believe this approach opens up interesting new research questions and is potentially applicable to commercial racing games.Keywords: Car racing, player modelling, entertainment metrics, content creation, evolution. I. THREE APPROACHES TO COMPUTATIONAL INTELLIGENCE IN GAMESMuch of the research done under the heading "computational intelligence and games" aims to optimise game playing strategies or game agent controllers. While these endeavours are certainly worthwhile, there are several other quite different approaches that could be at least as interesting, from both an academic and a commercial point of view.In this paper we discuss three approaches to computational intelligence in games: optimisation, imitation and innovation. We describe these approaches as they apply to games in general and exemplify them as they apply to racing games in particular. We then describe an experiment where these approaches are used in a racing game to augment player satisfaction. The taxonomy given below is of course neither final nor exhaustive, but it is a start. A. The optimisation approachMost research into computational intelligence and games takes the optimisation approach, which means that an optimisation algorithm is used to tune values of some aspect of the game. Examples abound of using evolutionary computation to develop good game-playing strategies, in all sorts of games from chess to poker to warcraftSeveral groups of researchers have taken this approach towards racing games. Tanev [3] developed an anticipatory control algorithm for an R/C racing simulator, and used evolutionary computation to tune the parameters of this algorithm for optimal lap time. Chaperot and Fyfe [4] evolved neural network controllers for minimal lap time in a 3D motocross game, and we previously ourselves investigated which controller architectures are best suited for such optimisation in a simple racing game [5]. Sometimes optimisation is multiobjective, as in our previous work on optimising controllers for performance on particular racing tracks versus robustness in driving on new tracks [6]. And there are other things than controllers that can be optimised in car racing, as is demonstrated by the work of Wloch and Bentley, who optimised the parameters for simulated Formula 1 cars in a physically sophisticated racing game [7].While games can be excellent test-beds for evolutionary and other optimisation algorithms, it can be argued that improving game-playing agents is in itself of little practical value...