CS 591, Section EA Advanced Reasoning in AI Autumn 2004 Tentative Course Syllabus

Session	Date	Topic/Readings	Sample Application Reading	Assignment
2	Sep 7	Learning partially observable action models		Eyal Amir
3	Sep 14	Aggregating Learned Probabilistic Beliefs (ps)		Pedrito Maynard-Zhang
4	Sep 21	Compiling control knowledge into preconditions for planning in the situation calculus		Phil Oertel
5	Sep 28	Automation of proof by mathematical induction (book chapter from Handbook)		Ken Keefe
6	Oct 5	Efficiently Inducing Features of Conditional Random Fields, Andrew McCallum. UAI 2003		Abhishek Tiwari
7	Oct 12	Linear belief functions		Jeff Pasternack
8	Oct 19	Extending KB approach to Planning with sensing		Hannaneh Hajishirzi
9	Oct 26	Generalized version of resolution		Deepak Ramachandran
10	Nov 2	Logic of motion		Chi Trinh
11	Nov 9	Representing beliefs in fluent calculus		Afsaneh Shirazi
12	Nov 16	Frame problem and Bayesian Networks		Brian Hlubocky
13	Nov 30	Learning in Bayesian Networks / Learning to reason / hardness of approximate reasoning		Steve Lauderberg
14	Dec 7	Case-factor diagrams for structured probabilistic modeling		Tony Bergstron

Viewing PostScript and PDF

Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.