Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
This book presents sequential decision theory from a novel algorithmic information theory perspective. Get and download textbook Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability for free
Categories: Algorithms, Artificial intelligence, Algorithms. Contributors: Marcus Hutter - Author. Format: NOOK Study
While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence Universal Artificial Intelligence new edition
Download free books for Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability: Jeffrey P. Thomas, Kenneth C. Hall, Robert E. Kielb
Categories: Algorithms, Computer graphics, Artificial intelligence. Contributors: Marcus Hutter - Author. Format: Hardcover
Categories: Algorithms, Computer graphics, Artificial intelligence. Contributors: Marcus Hutter - Author. Format: Hardcover
Author: Hutter, Marcus ISBN-10: 3540221392
Universal Artificial Intelligence Textbook
While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning
hile the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments. The book introduces these two different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence
