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The Imtimate Machine

 
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Model Behavior 3 pages: | 1 | 2 | 3 |
By Bruce Blumberg, a professor in the MIT Media Lab
Excerpted from "D-Learning: What learning in dogs tells us about building characters that learn what they ought to learn," Exploring Artificial Intelligence in the New Millennium, Morgan Kaufman Publishers, July 2002.

October 22 , 2002
Duotone of PuppyI
n "Alpha Wolf," Alan Alda checks in with Bruce Blumberg during a return visit to The Media Lab at MIT. Blumberg has long been inspired by dogs in his efforts to build autonomous animated creatures that can learn. As Alan has witnessed firsthand, Blumberg's virtual canines can be trained to respond to voice commands just like a real animal. But why has Blumberg repeatedly chosen dogs as his model? A dog-lover and owner of a silky terrier named Sydney, Blumberg believes that dogs possess the ideal qualities we humans look for in a companion, virtual or real. So perhaps "man's best friend" has something to teach us about how to make computers more sociable.
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Meeting Expectations

People expect a level of common sense from any animate system - that it will "get the good" and "avoid the bad," given its desires, repertoire of actions, and beliefs about how the world works. Part of the common sense expected from an intelligent system is that it will learn from experience. Indeed, the ability to learn from experience is one measure of what people often label as intelligence, i.e., more intelligent creatures are better able to learn than less intelligent ones. When a character doesn't learn from experience, we are left wondering "is it stupid, or is it simply broken?"

Photo of Duncan the Virtual Dog
Blumberg's goal is to make virtual dogs like Duncan as trainable as a real dog.
 

The goal of the Synthetic Characters Group at the Media Lab of MIT is to understand how to build autonomous animated characters that possess the everyday common sense, ability to learn, and apparent sense of empathy that one finds in animals such as dogs. That is, we take our fundamental inspiration from animal learning and training. Our belief is that by paying close attention to how animals learn and to successful techniques by which they are trained, we can not only improve on existing models for machine learning, but also develop robust techniques for real-time learning in autonomous animated characters.


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3 pages: | 1 | 2 | 3 |

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