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Communications of the ACM
Realistic modeling and rendering of plant ecosystems
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Using frustration in the design of adaptive videogames
Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology
Adaptive game AI with dynamic scripting
Machine Learning
Computer
Boredom, engagement and anxiety as indicators for adaptation to difficulty in games
Proceedings of the 12th international conference on Entertainment and media in the ubiquitous era
The role of semantics in games and simulations
Computers in Entertainment (CIE) - SPECIAL ISSUE: Media Arts (Part II)
Online action adaptation in interactive computer games
Computers in Entertainment (CIE) - SPECIAL ISSUE: Media Arts and Games (Part II)
Defining the Semantics of Conceptual Modeling Concepts for 3D Complex Objects in Virtual Reality
Journal on Data Semantics XIV
Polymorph: dynamic difficulty adjustment through level generation
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
Rules of engagement: moving beyond combat-based quests
Proceedings of the Intelligent Narrative Technologies III Workshop
Action recognition for support of adaptive gameplay: a case study of a first person shooter
International Journal of Computer Games Technology
Experience-Driven Procedural Content Generation
IEEE Transactions on Affective Computing
Knowledge in the loop: semantics representation for multimodal simulative environments
SG'05 Proceedings of the 5th international conference on Smart Graphics
Using gameplay semantics to procedurally generate player-matching game worlds
Proceedings of the The third workshop on Procedural Content Generation in Games
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Adaptive games are expected to improve on the pre-scripted and rigid nature of traditional games. Current research uses player and experience modeling techniques to successfully predict some game-play adjustments players desire. These are typically deployed to adapt AI behavior or to evolve content for simple game levels. In this paper we propose a generation framework aimed at creating personalized content for complex and immersive game worlds. This framework, currently under development, captures which content provided the context for a given personal gameplay experience. This model is then used to generate content for the next predicted experience, through retrieval and recombination of semantic gameplay descriptions, i.e. case-based mappings between content and player experience. Through its integration with existing player and experience modeling techniques, this framework aims at generating, in an emergent way, game worlds that better suit players. Dynamic game content, which responds to the player performance, has the ability to personalize player experience, potentially making games even more unpredictable and fun.