Planning with Markov Decision Processes

An AI Perspective

Häftad, Engelska, 2012

Av Mausam, Andrey Kolobov, Mausam, Kenneth A. Loparo, Mausam Natarajan

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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

Produktinformation

Utgivningsdatum2012-07-03
Mått191 x 235 x 17 mm
Vikt406 g
FormatHäftad
SpråkEngelska
SerieSynthesis Lectures on Artificial Intelligence and Machine Learning
Antal sidor194
FörlagSpringer International Publishing AG
ISBN9783031004315
OriginaltitelPlanning with Markov Decision Processes

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Tillämpad matematik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

Mausam is a Research Assistant Professor at the Turing Center in the Department of Computer Science at the University of Washington, Seattle. His research interests span various sub-fields of artificial intelligence, including sequential decision making under uncertainty, large scale natural language processing, Web information systems, heuristic search, machine learning, and AI applications to crowd-sourcing. Mausam obtained a Ph.D. from University of Washington in 2007 and a Bachelor of Technology from IIT Delhi in 2001. His PhD thesis was awarded honorable mention for the 2008 ICAPS Best Dissertation Award. Mausam has written scores of papers in top AI conferences and journals. He has served on the senior program committees of AI conferences such as AAAI and IJCAI, program committees of several other conferences, and on NSF panels.