Algorithms for solving sequential
(zero-sum) games
Main case in these slides: chess
!
Slide pack by"
Tuomas Sandholm
"
Rich history of cumulative ideas
Game-theoretic perspective
"
Game of perfect information"
Finite game"
Finite action sets"
Finite
4/8/13
Learning to Win:
Thresholded Rewards
Manuela M. Veloso
Carnegie Mellon University
Computer Science Department
15-780 Graduate AI Spring 2013
Thanks to Colin McMillen
Readings
STP: Skills, tactics and plays for multi-robot control in adversarial
en
4/2/13
Probabilistic Robot Path Planning
Manuela M. Veloso
Carnegie Mellon University
Computer Science Department
15-780 Graduate AI Spring 2013
Readings:
Real-time randomized path planning for robot
navigation, James Bruce and Manuela Veloso.
In Proceed
4/1/13
Bayes Networks: Representation
and Inference
Manuela M. Veloso
Carnegie Mellon University
Computer Science Department
Thanks to past instructors
15-780 Graduate AI Spring 2013
Readings:
Russell & Norvig: chapter 14
Bayes Rule
P (A | B) = P (A , B
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 20, 2012
Today:
Logistic regression
Generative/Discriminative
classifiers
Readings: (see class website)
Required:
Mitchell: Nave Bayes and
Logistic
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
September 25, 2012
Today:
Linear regression
Bias/Variance/Unavoidable
errors
Readings:
Required:
Bishop: Chapt. 1 through 1.2.5
Bishop: Chapt. 3 through 3.2
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
October 4, 2012
Today:
Readings:
Graphical models
Bayes Nets:
Inference
Learning
Required:
Bishop chapter 8
Bayesian Networks Definition
A Bayes network re
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
October 9, 2012
Today:
Readings:
Graphical models
Bayes Nets:
Inference
Learning
Required:
Bishop chapter 9 through 9.2
Estimate
from partly observed data
Machine Learning 10-601
Tom M. Mitchell
Machine Learning Department
Carnegie Mellon University
October 11, 2012
Today:
Recommended reading:
Computational Learning
Theory
Probably Approximately
Coorrect (PAC) learning
theorem
Vapnik-Chervonenkis (VC)
di