Introduction to Statistical Machine Learning
Selective attention in RL B. Ravindran Joint work with Shravan Matthur, Vimal Mathew, Sanjay Karanth, Andy Barto.
Eick/Alpaydin Introduction
A Novel Specification and Composition Language for Services
Detection of Channel Stuffing
Reinforcement Learning I: The setting and classical stochastic dynamic programming algorithms Tuomas Sandholm Carnegie Mellon University Computer Science.
PLANNING Ivan Bratko Acknowledgement: Some of these slides were adapted from D. Nau’s course on planning.
FOL resolution strategies Tuomas Sandholm Carnegie Mellon University Computer Science Department [Finish reading Russell & Norvig Chapter 9 if you haven’t.
ROBOT BEHAVIOUR CONTROL SUCCESSFUL TRIAL OF MARKERLESS MOTION CAPTURE TECHNOLOGY Student E.E. Shelomentsev Group 8Е00 Scientific supervisor Т.V. Alexandrova.
REPLANNING IN DOMAINS WITH PARTIAL INFORMATION AND SENSING ACTIONS Guy Shani Ronen Brafman Ben-Gurion University 1 ProblemBackgroundSDR Results.
Oct 17, 2006Sudeshna Sarkar, IIT Kharagpur1 Machine Learning Sudeshna Sarkar IIT Kharagpur.
Hierarchical Reinforcement Learning Ronald Parr Duke University ©2005 Ronald Parr From ICML 2005 Rich Representations for Reinforcement Learning Workshop.
