Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Made-up minds: a constructivist approach to artificial intelligence
Made-up minds: a constructivist approach to artificial intelligence
Reinforcement learning architectures for animats
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Technical Note: \cal Q-Learning
Machine Learning
Fundamentals of speech recognition
Fundamentals of speech recognition
An introduction to natural computation
An introduction to natural computation
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Expectation Maximization for Weakly Labeled Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning in embedded systems
A layered brain architecture for synthetic creatures
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Developing an aesthetic: character-based interactive installations
ACM SIGGRAPH Computer Graphics
Affective Learning — A Manifesto
BT Technology Journal
Using anticipation to create believable behaviour
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
A fundamental capability of compelling autonomous animated characters is the ability to learn from experience and to alter their observable behavior accordingly. In this chapter, I highlight important lessons from animal learning and training, from machine learning, and from the incorporation of learning into digital pets. I then briefly present an approach, informed by the lessons above, toward building characters that learn. Finally, I discuss a number of installations we have built that feature characters that learn what they ought to learn.