CS 660: Computational Decision Making

http://www.cs.uky.edu/~goldsmit/cdm/syl.html

Syllabus

Time and Place: 12:30--1:45 Tues/Thurs.

Professor: Dr. J. Goldsmith
Office: 763E Anderson Hall; Phone: 859-257-4245
Office Hours: TBA or by appointment. Email questions encouraged and answered.

Course Description:

Prereqs: You must be able to program in at least one high-level language.

Textbooks:
I couldn't find one. See the page of links for additional readings and pointers to related books.

Grading:
will be by short weekly assignments, including problem sets, short programs, and applications of existing software, and two longer assignments.

READ THIS:

Attendance in class and section is very strongly encouraged.

Copying of homework from other students or from other sources is strictly prohibited. Obtaining a solution from another source without citing the source is plagiarism. You are encouraged to visit Dr. Goldsmith in her office hours or to send her email if you are stuck on homework problems. You do not need an appointment for regularly scheduled hours.

Approximate Week by Week Course Outline:

Date	Topic	Readings	Assignment
Jan. 14	Introduction to the course: what is decision-making?	What is [human] decision making?	First assignment
Jan. 19	Computational issues: knowledge representation	Intro to KR (AAAI's AI Topics) Representing and Reasoning with Preferences, by Walsh
Jan. 21	Computational issues: algorithms and complexity	Computational complexity links	Second assignment
Jan. 26	Computational issues: modeling and model acquisition
Jan. 28	Types of decision-making: planning, scheduling, inference, recommender systems, decision support
Feb. 2	Types of decision-making: mixed-initiative planning, multi-agent systems, preference aggregation, and voting	Logical preference representation and combinatorial vote, by Jerome Lang
Feb. 2	First Project Proposal Due!
Feb. 4	Knowledge representation: data structures		Third assignment
Feb. 9	Knowledge representation: reasoning systems	Knowledge Representation and Classical Logic, by Vladimir Lifschitz, Leora Morgenstern and David Plaisted and Logical Agents, by Russell and Norvig
Feb. 11, 16	Knowledge acquisition: data mining, machine learning, knowledge elicitation	Data Mining and Clinical Decision Support Systems by Hardin and Chhieng; Preference Elicitation with Subjective Features, by Boutilier, Regan and Viappiani; User-Involved Preference Elicitation for Product Search and Recommender Systems by Pu and Chen; Ordinal judgments in multiattribute decision analysis Moshkovich, Mechitov, and Olson	Fourth assignment
18	Planning	Automated Planning, by Nau
Feb. 24--26	Scheduling	John Williams' slides from an OS course and Flowshop algorithm survey by Minella, Ruiz, and Ciavotta, and Karp's '65 NP-completness paper
Mar. 2	Inference	Stuart Russell's inference slides
Mar. 4	Inference in probabilistic systems	Technical Introduction: A Primer on Probabilistic Inference by Griffiths and Yuille
Mar. 4	First Project Due!
Mar. 9--11	Planning in probabilistic systems	A page of links, and a link to A. Moore's Tutorial Slides	Fifth assignment
Mar. 13--21	SPRING BREAK
Mar. 26	Second Project Proposal Due
Mar. 23--25	Recommender systems	Zanker and Jannach's tutorial at SAC 2010	Sixth assignment
Mar. 30--Apr. 1	Decision support systems	Intelligent decision support
Apr. 6--8	Mixed initiative planning	Evaluating mixed-initiative systems, by Cortellessa and Cesta Visual tools for mixed-initiative systems" by Pu and Lalanne
Apr. 13--15	Preference aggregation	Preference article by Walsh; Conitzer's dissertation	Seventh assignment
Apr. 20--22	Multi-criteria decision making	Multiobjective Optimization by Matthias Ehrgott
Apr. 27--29	Voting	Computational Social Choice by Lang, et al.
Apr. 29	Final Project Due!
May 4th	Course Summary