Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Modeling motivations and emotions as a basis for intelligent behavior
AGENTS '97 Proceedings of the first international conference on Autonomous agents
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 adaptive characters in open-ended simulation games
Proceedings of the 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
Computational Affective Sociology
Affect and Emotion in Human-Computer Interaction
Modelling Behaviour Cycles for Life-Long Learning in Motivated Agents
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Towards a computational model of creative societies using curious design agents
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
Multi-Agent cooperative reinforcement learning in 3d virtual world
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Agent-Based Virtual Humans in Co-Space: An Evaluative Study
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning object repurposing for various multimedia platforms
Multimedia Tools and Applications
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Massively multiplayer online computer games are played in complex, persistent virtual worlds. Over time, the landscape of these worlds evolves and changes as players create and personalise their own virtual property. In contrast, many non-player characters that populate virtual game worlds possess a fixed set of pre-programmed behaviours and lack the ability to adapt and evolve in time with their surroundings. This paper presents motivated reinforcement learning agents as a means of creating non-player characters that can both evolve and adapt. Motivated reinforcement learning agents explore their environment and learn new behaviours in response to interesting experiences, allowing them to display progressively evolving behavioural patterns. In dynamic worlds, environmental changes provide an additional source of interesting experiences triggering further learning and allowing the agents to adapt their existing behavioural patterns in time with their surroundings.