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Learning in Embedded Systems

Online Resource
193 Seiten
2019
Bradford Books (Hersteller)
9780262288507 (ISBN)
CHF 55,10 inkl. MwSt
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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.

Leslie Pack Kaelbling is Assistant Professor in the Computer Science Department at Brown University.

Erscheint lt. Verlag 20.6.2019
Verlagsort Massachusetts
Sprache englisch
Maße 157 x 226 mm
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
ISBN-13 9780262288507 / 9780262288507
Zustand Neuware
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