CIS 730: Artificial Intelligence

Fall 2004

 

Hours: 3 hours (additional 3-hour proseminar in graphical models, CIS798, available)

Prerequisite: CIS 300 and 501 or equivalent coursework in data structures and algorithms; CIS 301 (set theory/logic), Math 510 (discrete math), Stat 410 (intro probability) recommended

Textbook: Artificial Intelligence: A Modern Approach, 2nd edition, S. J. Russell and P. Norvig.  Prentice-Hall, 2004.  ISBN: 0137903952

Time and Venue: Mon, Wed, Fri 13:30 - 14:20, Room 127 Nichols Hall (N127)

Instructor: William H. Hsu, Department of Computing and Information Sciences

Office: N213            Office phone: (785) 532-6350 x29            Home phone: (785) 539-7180

URL: http://www.cis.ksu.edu/~bhsu               E-mail: cis730ta@www.kddresearch.org

Office hours:    10:30-11:30 Mon, Wed, Fri;

by appointment, Friday after 16:00

Class web page: http://www.kddresearch.org/Courses/Fall-2004/CIS730/

 

Course Description

 

This course provides fundamental background in intelligent systems for graduate and advanced undergraduate students.  Topics to be covered include intelligent agents, problem-solving, uninformed and informed (heuristic) search, logical and probabilistic knowledge representation, logical and probabilistic inference, foundations of classical and universal planning, essentials of machine learning, and a brief survey of computer vision and natural language processing (NLP).  Applications to practical design and development of intelligent systems will be emphasized, leading to team projects on current topics and applications in AI.

 

Course Requirements

 

Homework (20%): 5 out of 6 programming and written assignments (20%)

Examinations (50%): 2 in-class hour exams (10% each), 1 in-class final (30%)

Project (25%): term programming project and report for all students (25%)

Class participation (5%): class and online discussions, asking and answering questions (5%)

 

Computer language(s): C/C++, Java, or student choice (upon instructor approval)

 

Selected reading (on reserve in K-State CIS Library)

 

·         Artificial Intelligence, 2nd ed., E. Rich and K. Knight.  McGraw-Hill, 1990.  ISBN: 0070522634

·         Essentials of Artificial Intelligence, M. Ginsberg.  Morgan-Kaufman, 1993.  ISBN: 1558602216

·         Logical Foundations of Artificial Intelligence, N. J. Nilsson and M. R. Genesereth.  Morgan-Kaufmann, 1987. ISBN: 0934613311

Syllabus

 

Lecture

Date

Topic

(Primary) Source

0

2004 Aug 18

Administrivia; overview of topics

RN Chapter 1

1

2004 Aug 20

Intelligent agents and problem solving

RN Chapter 2

2

2004 Aug 23

Intro to Search and Constraints

RN 3, Appendix A

3

2004 Aug 25

Uninformed search: DFS, BFS, B&B

RN Chapter 3

4

2004 Aug 27

Constraint Satisfaction Problems

RN Chapter 3

5

2004 Aug 30

Informed search: hill-climbing, beam

RN Chapter 4

6

2004 Sep 01

Informed search: A*

RN Chapter 4

7

2004 Sep 03

AI Applications 1: Interaction, Games

RN Chapter 5

8

2004 Sep 08

Game tree search: intro

RN Chapter 5

9

2004 Sep 10

Game tree search: conclusion

RN Chapter 5

10

2004 Sep 13

Knowledge representation

RN Chapter 6

11

2004 Sep 15

Intro to propositional logic

RN Chapter 7

12

2004 Sep 17

Propositional inference

RN Chapter 7

13

2004 Sep 20

Production systems

RN Chapter 6

14

2004 Sep 22

Predicates and relations

RN Chapter 7

15

2004 Sep 24

First-order logic

RN Chapter 7

16

2004 Sep 27

First-order knowledge bases

RN Chapter 8

17

2004 Sep 29

Clausal (Conjunctive Normal) Form

RN Chapter 9

18

2004 Oct 01

Unification

RN Chapter 9

19

2004 Oct 04

Resolution theorem-proving / review

RN Chapter 8-9

20

2004 Oct 06

Theorem-proving and decidability

RN Chapter 9

21

2004 Oct 08

Logic programming

RN Chapter 9-10

22

2004 Oct 13

Hour Exam 1 (Closed-Book)

RN Chapters 2-10

23

2004 Oct 15

Classical planning / midterm review

RN Chapter 11

24

2004 Oct 18

More classical planning

RN Chapter 11

25

2004 Oct 20

Hierarchical abstraction planning

RN Chapter 12

26

2004 Oct 22

Conditional planning and replanning

RN Chapter 13

27

2004 Oct 25

Universal and reactive planning

RN Chapter 13

28

2004 Oct 27

Uncertainty and probabilistic reasoning

RN Chapter 14-15

29

2004 Oct 29

Probability review; Bayesian inference

RN Chapter 14

30

2004 Nov 01

Intro to graphical models, Part I

RN Chapter 15

31

2004 Nov 03

Intro to graphical models, Part II

RN Chapter 15

32

2004 Nov 05

AI Applications 2: Machine Translation

–

33

2004 Nov 08

Inference in graphical models

RN Chapter 15

34

2004 Nov 10

Introduction to machine learning

RN 18; TMM 1-2

35

2004 Nov 12

Machine learning basics / review

RN 18-19, TMM 1-2

36

2004 Nov 15

Decision trees

TMM 3

37

2004 Nov 17

Hour Exam 2 (Closed-Book)

RN Chapters 11-15, 18

38

2004 Nov 19

AI Applications 3: Robotics and HCI

–

39

2004 Nov 22

Learning to reason; philosophical issues

RN Chapter 26

40

2004 Nov 29

Ramifications of AI

RN Chapter 26

41

2004 Dec 01

Vision and perception

RN Chapter 24

42

2004 Dec 03

Project presentations

–

43

2004 Dec 06

Project presentations

–

44

2004 Dec 08

Project presentations

–

45

2004 Dec 10

Final review

RN 2-12, 14-15, 18-19

46

TBD

FINAL EXAM (OPEN-BOOK)

RN 2-12,14-15,18-19

 

RN: Artificial Intelligence: A Modern Approach, 2nd Edition, S. J. Russell and P. Norvig

TMM: Machine Learning, T. M. Mitchell

Lightly-shaded entries denote the due date of a written problem set.

Heavily-shaded entries denote the due date of a machine problem (programming assignment).