Towards automatic personalized content generation for platform games

In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.peer-reviewe

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

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty 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 di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Media fusion and future TV: Examining multi-screen TV convergence in Singapore

This study examines Singapore's national media blueprint and industry stakeholders' coping strategies in response to multi-screen TV development. The findings show Singapore muti-screen TV development is still at a nascent stage after launching Media Fushion and FutureTV plans in mid 2009. The policymakers play a key role to follow national media blueprint to unify the inter-industry and cross-country collaboration. TV operators and telcos are found to remediate themselves by harnessing the power of internet and mobile technologies for content innovation and distribution. To tackle the complicated convergent issues in multi-screen TV industry, this study proposes to separately regulate the technology-neutral platforms and diverse audiovisual content. It also recommends a pro-innovative policy with the light-touch licensing scheme and loose content regulation to facilitate the development of the next TV. --three-screen TV,multi-screen TV,convergence,media fusion,IPTV,mobile TV,cross-platform,TV technologies,TV market,TV policy

Modern Trends in the Automatic Generation of Content for Video Games

Attractive and realistic content has always played a crucial role in the penetration and popularity of digital games, virtual environments, and other multimedia applications. Procedural content generation enables the automatization of production of any type of game content including not only landscapes and narratives but also game mechanics and generation of whole games. The article offers a comparative analysis of the approaches to automatic generation of content for video games proposed in last five years. It suggests a new typology of the use of procedurally generated game content comprising of categories structured in three groups: content nature, generation process, and game dependence. Together with two other taxonomies – one of content type and the other of methods for content generation – this typology is used for comparing and discussing some specific approaches to procedural content generation in three promising research directions based on applying personalization and adaptation, descriptive languages, and semantic specifications

Experience-driven procedural content generation (extended abstract)

Procedural content generation is an increasingly important area of technology within modern human-computer interaction with direct applications in digital games, the semantic web, and interface, media and software design. The personalization of experience via the modeling of the user, coupled with the appropriate adjustment of the content according to user needs and preferences are important steps towards effective and meaningful content generation. This paper introduces a framework for procedural content generation driven by computational models of user experience we name Experience-Driven Procedural Content Generation. While the framework is generic and applicable to various subareas of human computer interaction, we employ games as an indicative example of content-intensive software that enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects C2Learn (318480) and iLearnRW (318803).peer-reviewe