MIT Press
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.
Kaelbling
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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.
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