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...
Κύριοι συγγραφείς: | , |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2009.
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Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.