Learning in embedded systems / by Leslie Pack Kaelbling.
By: Kaelbling, Leslie Pack [author.]
.
Contributor(s): IEEE Xplore (Online Service) [distributor.] | MIT Press [publisher.].
Material type: 
BookSeries: Report (Stanford University. Computer Science Department): no. STAN-CS-90-1326.Publisher: Stanford, Calif. : Dept. of Computer Science, Stanford University, [c1990]Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2008]Description: 1 PDF (xx, 200 pages) : illustrations.Content type: text Media type: electronic Carrier type: online resourceISBN: 9780262288507.Subject(s): Embedded computer systems -- Programming | Computer algorithmsGenre/Form: Electronic books.Additional physical formats: Print version: No titleDDC classification: 005.1 Online resources: Abstract with links to resource Also available in print.Summary: Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University.
Cover title.
"June 1990."
Includes bibliographical references (p. 191-200).
Restricted to subscribers or individual electronic text purchasers.
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases.Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University.
Also available in print.
Mode of access: World Wide Web
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