Reasoning with Probabilistic and Deterministic Graphical Models by Rina Dechter
Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general.-
Graph Representation Learning
-
A Concise Introduction to Models and Methods for Automated Planning
-
Learning with Support Vector Machines
-
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
-
Action Programming Languages
-
Human Computation
-
Learning and Decision-Making from Rank Data
-
Explainable Human-AI Interaction
-
Network Embedding
-
Predicting Human Decision-Making
-
Game Theory for Data Science
-
Strategic Voting
-
Statistical Relational Artificial Intelligence
-
A Short Introduction to Preferences
-
Case-Based Reasoning
-
Representing and Reasoning with Qualitative Preferences
-
Robot Learning from Human Teachers
-
Visual Object Recognition
-
General Game Playing
-
Essential Principles for Autonomous Robotics
-
Federated Learning
-
Graph-Based Semi-Supervised Learning
-
An Introduction to Constraint-Based Temporal Reasoning
-
Intelligent Autonomous Robotics
-
Answer Set Solving in Practice
-
Representation Discovery using Harmonic Analysis
-
Representations and Techniques for 3D Object Recognition and Scene Interpretation
-
Introduction to Intelligent Systems in Traffic and Transportation
-
Introduction to Symbolic Plan and Goal Recognition
-
Judgment Aggregation
-
Metric Learning
-
Data Integration
-
Trading Agents
-
Introduction to Semi-Supervised Learning
-
Transfer Learning for Multiagent Reinforcement Learning Systems
-
Introduction to Graph Neural Networks
-
Introduction to Logic Programming
-
An Introduction to the Planning Domain Definition Language
-
Lifelong Machine Learning, Second Edition
-
Adversarial Machine Learning
-
Multi-Objective Decision Making
-
Active Learning
-
Planning with Markov Decision Processes
-
Computational Aspects of Cooperative Game Theory
-
Algorithms for Reinforcement Learning
Rina Dechters research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing, and probabilistic reasoning. She is a Chancellors Professor of Computer Science at the University of California, Irvine. She holds a Ph.D. from UCLA, an M.S. degree in applied mathematics from the Weizmann Institute, anda B.S. in mathematics and statistics from the Hebrew University in Jerusalem. She is the author of Constraint Processing published by Morgan Kaufmann (2003), and of Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms published by Morgan and Claypool (2013). She has co-authored close to 200 research papers and has served on the editorial boards of:Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research (JAIR), and Journal of Machine Learning Research (JMLR). She is a Fellow of the American Association of Artificial Intelligence since 1994, was a Radcliffe Fellow during 2005-2006, received the 2007 Association of Constraint Programming (ACP) Research Excellence Award, and became an ACM Fellow in 2013. She was a Co-Editor-in-Chief of Artificial Intelligence from 2011 to 2018 and is the conference chair-elect for IJCAI-2022.
| SKU | Unavailable |
| ISBN 13 | 9783031004551 |
| ISBN 10 | 3031004558 |
| Title | Reasoning with Probabilistic and Deterministic Graphical Models |
| Author | Rina Dechter |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2019-02-14 |
| Number of pages | 185 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |