Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Motivated reinforcement learning for non-player characters in persistent computer game worlds
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Designing Toys That Come Alive: Curious Robots for Creative Play
ICEC '08 Proceedings of the 7th International Conference on Entertainment Computing
Proposal of Exploitation-Oriented Learning PS-r#
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Modelling Behaviour Cycles for Life-Long Learning in Motivated Agents
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
The penalty avoiding rational policy making algorithm in continuous action spaces
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Gamification, Serious Games, Ludic Simulation, and other Contentious Categories
International Journal of Gaming and Computer-Mediated Simulations
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Recently a new generation of virtual worlds has emerged in which users are provided with open-ended modelling tools with which they can create and modify world content. The result is evolving virtual spaces for commerce, education and social interaction. In general, these virtual worlds are not games and have no concept of winning, however the open-ended modelling capacity is nonetheless compelling. The rising popularity of open-ended virtual worlds suggests that there may also be potential for a new generation of computer games situated in open-ended environments. A key issue with the development of such games, however, is the design of non-player characters which can respond autonomously to unpredictable, open-ended changes to their environment. This paper considers the impact of open-ended modelling on character development in simulation games. Motivated reinforcement learning using context-free grammars is proposed as a means of representing unpredictable, evolving worlds for character reasoning. This technique is used to design adaptive characters for the Second Life virtual world to create a new kind of open-ended simulation game.