Learning in Embedded Systems is useful for learning to perform complex action strategies in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, author Leslie Pack Kaelbling explores algorithms that learn efficiently from trial-and error experience with an external world. This book 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:

• The interval estimation 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
• Some of the first reinforcement-learning experiments with a real robot

240 pages, hardcover

About the Author

Leslie Pack Kaelbling is Professor of Computer Science and Engineering at the Massachusetts Institute of Technology.