Motivated Reinforcement Learning Curious Characters for Multiuser Games /

Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments - the goal being to understand how machines can develop new skills an...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Merrick, Kathryn E. (Autor), Maher, Mary Lou (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Temas:
Artificial intelligence.
User interfaces (Computer systems).
Human-computer interaction.
Computer graphics.
Image processing-Digital techniques.
Computer vision.
Computer-aided engineering.
Artificial Intelligence.
User Interfaces and Human Computer Interaction.
Computer Graphics.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer-Aided Engineering (CAD, CAE) and Design.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Non-Player Characters and Reinforcement Learning
  • Non-Player Characters in Multiuser Games
  • Motivation in Natural and Artificial Agents
  • Towards Motivated Reinforcement Learning
  • Comparing the Behaviour of Learning Agents
  • Developing Curious Characters Using Motivated Reinforcement Learning
  • Curiosity, Motivation and Attention Focus
  • Motivated Reinforcement Learning Agents
  • Curious Characters in Games
  • Curious Characters for Multiuser Games
  • Curious Characters for Games in Complex, Dynamic Environments
  • Curious Characters for Games in Second Life
  • Future
  • Towards the Future.

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