Planning with Markov Decision Processes by Mausam
Introduction.- MDPs.- Fundamental Algorithms.- Heuristic Search Algorithms.- Symbolic Algorithms.- Approximation Algorithms.- Advanced Notes.-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
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.
| SKU | Unavailable |
| ISBN 13 | 9783031004315 |
| ISBN 10 | 3031004310 |
| Title | Planning with Markov Decision Processes |
| Author | Mausam |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2012-07-03 |
| Number of pages | 194 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |