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9780262111744

Learning in Embedded Systems

Learning in Embedded Systems
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  • Comments: Significant staining, on the other hand, might suggest exposure to various environments and situations. Whether it be a coffee stain from a leisurely morning read or water damage from a rainy day commute, each mark contributes to the book?s unique history. While some might view these imperfections as detriments, others see them as tangible connections to the book's past, enhancing its authenticity and charm.

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  • ISBN-13: 9780262111744
  • ISBN: 0262111748
  • Publisher: MIT Press

AUTHOR

Kaelbling, Leslie P.

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.Kaelbling, Leslie P. is the author of 'Learning in Embedded Systems' with ISBN 9780262111744 and ISBN 0262111748.

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