Towards automatic personalised content creation for racing games

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

Towards player-driven procedural content generation

Generating immersive game content is one of the ultimate goals for a game designer. This goal can be achieved by realizing the fact that players' perception of the same game differ according to a number of factors including: players' personality, playing styles, expertise and culture background. While one player might find the game immersive, others may quit playing as a result of encountering a seemingly insoluble problem. One promising avenue towards optimizing the gameplay experience for individual game players is to tailor player experience in real-time via automatic game content generation. Specifying the aspects of the game that have the major influence on the gameplay experience, identifying the relationship between these aspect and each individual experience and defining a mechanism for tailoring the game content according to each individual needs are important steps towards player-driven content generation.peer-reviewe

Towards the automatic generation of card games through Grammar-Guided Genetic Programming

We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game

Personas versus clones for player decision modelling

The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce play traces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play traces.peer-reviewe

AudioInSpace : exploring the creative fusion of generative audio, visuals and gameplay

Computer games are unique creativity domains in that they elegantly fuse several facets of creative work including visuals, narra- tive, music, architecture and design. While the exploration of possibil- ities across facets of creativity o ers a more realistic approach to the game design process, most existing autonomous (or semi-autonomous) game content generators focus on the mere generation of single domains (creativity facets) in games. Motivated by the sparse literature on mul- tifaceted game content generation, this paper introduces a multifaceted procedural content generation (PCG) approach that is based on the in- teractive evolution of multiple arti cial neural networks that orchestrate the generation of visuals, audio and gameplay. The approach is evaluated on a spaceship shooter game. The generated artifacts|a fusion of audio- visual and gameplay elements | showcase the capacity of multifaceted PCG and its evident potential for computational game creativity.This re-search is supported, in part, by the FP7 ICT project C2Learn (project no: 318480) and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

Digging deeper into platform game level design : session size and sequential features

A recent trend within computational intelligence and games research is to investigate how to affect video game players’ in-game experience by designing and/or modifying aspects of game content. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining.peer-reviewe

Designer modeling for personalized game content creation tools

With the growing use of automated content creation and computer-aided design tools in game development, there is potential for enhancing the design process through personalized interactions between the software and the game developer. This paper proposes designer modeling for capturing the designer’s preferences, goals and processes from their interaction with a computer- aided design tool, and suggests methods and domains within game development where such a model can be applied. We describe how designer modeling could be integrated with current work on automated and mixed- initiative content creation, and envision future directions which focus on personalizing the processes to a designer’s particular wishes.peer-reviewe

Interactive genetic engineering of evolved video game content

Procedural content generation techniques can increase replayability and lower the burden on developers by satisfying players\u27 demand for new content. However, procedural content also creates an opportunity for new kinds of player-driven content customization by giving players access to the parameterized content space. This paper presents such a technique that enables players to manually customize evolved content represented by artificial neural networks. In particular, particle system weapons evolved by the multiplayer space shooter called Galactic Arms Race (GAR) can be further genetically engineered by the players themselves in a new extension to the game called the Weapons Lab. Results are presented that demonstrate procedural weapons evolved by the game that are further customized by players in the Weapons Lab. The implication is that procedurallygenerated content of many types can also be customized by players, adding a further dimension to its potential appeal. Copyright 2010 ACM