`Fall 2006

CSUDH Computer Science Department

CSC411 Artificial Intelligence

 

Instructor:                  Jianchao (Jack) Han

Phone number:           x2624

Office:                         NSM A-133

Email:                         jhan@csudh.edu

Meeting time:            Tuesdays and Thursdays 1:00pm -2:15pm

Class room:                NSM D-129

Office hours:              MW 11:30pm – 1:30pm or by appointment

Course Website:        http://www.csc.csudh.edu/jhan/Fall2006/csc411

 

Prerequisites:             CSC123, CSC311, MAT271 and MAT281, MATH361

 

Text/Reference:         Artificial Intelligence: Structures and Strategies for Complex Problem Solving, George F. Luger, 5th Ed., Addison-Wesley, 2005. ISBN 0 321 26318 9

 

Content: This course will provide a comprehensive introduction to artificial intelligence (AI) and discuss its foundations and core concepts, principles, mechanisms and algorithms. We begin with a brief history of AI research in philosophy, psychology, and other related areas and introduce the background for understanding the issues addressed in modern research.  Four parts of this course will include 1) the research tools for AI problem solving, 2) knowledge representation and reasoning for AI, 3) AI programming in Prolog, and 4) advanced AI topics in machine learning and automated reasoning

 

Objectives: The objectives of this course is to help students

·        learn the history of AI in philosophy, psychology, logics, and mathematics,

·        enhance the abilities of using predicate calculus to describe features of a problem,

·        master the search – principles, algorithms and data structures used to implement search

·        understand the principles of search-based problem solving,

·        understand the essential role of heuristics in search-based problem solving,

·        know the stochastic methodology, fuzziness, and non-monotonic systems for reasoning in situations of uncertainty,

·        be familiar with the software architecture – production system – for implementing various search algorithms,

·        be able to use Prolog to implement simple AI systems

·        understand other knowledge representation technologies such as semantic networks,  frames, scripts,

·        understand the architecture and mechanism of expert systems,

·        understand the concepts, tasks, and techniques of symbol-based machine learning,

·        understand the basic design of robots

·        understand the architecture and mechanism of artificial neutral networks,

·        understand the principle and mechanism of automated reasoning

 

 Requirements: There will be ONE midterm tests and ONE final examination. All tests will be written in class. FOUR written assignments and THREE programming projects will be required. To pass this course, you MUST complete ALL examinations, AT LEAST THREE written assignments and TWO programming projects.

 

Grading: The following weights will be applied to calculate your final score:

·        Four written assignments 20%, 5% each

·        Three programming projects 30%, 10% each

·        One midterm tests 20%

·        One final examination 30%

 

The score will be mapped to your final grade as follows:

Range

Grade

Range

Grade

Range

Grade

[95, 100]

A

[75, 80)

B-

[55, 60)

D+

[90, 95)

A-

[70, 75)

C+

[50, 55)

D

[85, 90)

B+

[65, 70)

C

[0, 50)

F

[80, 85)

B

[60, 65)

C-

 

 

 

Academic Integrity: Academic integrity is very important for all courses including this one at CSUDH. You are obliged to consult appropriate sections of the University Catalog and obey all rules relevant to its lawful missions, processes, and functions. Unless specifically stated otherwise in this syllabus, all written exams and programming projects must be the students' own work. Plagiarism and cheating (e.g. stealing or copying the work of others and turning it in as your own) will not be tolerated, and will be dealt with according to the University policy.

 

Attendance: Students are expected and encouraged to attend lectures and contribute to discussions. It is the student’s responsibility to contact the instructor as early as possible if he/she cannot attend the class to write exams. With convinced reasons, the instructor might arrange make-up midterm exams, but not final exam.

 

Drop Policy: The students should follow the course drop policy of the University. The last day to drop Without Record of Enrollment or from FT to PT Status with Refund is Thursday, September 14, 2006, after which the drop/withdraw will be reflected in the students’ record of enrollment. The last day to drop/withdraw with serious and compelling reason is Thursday, November 16, 2006, after which the serious accident/illness will be required to drop/withdraw.

 

Tentative Class Schedule (subject to change):

 

Week 1: Chapter 1 – AI: Early history and applications

 

Week 2: Chapter 2 -- The predicate calculus

·        Written assignment 1

 

Week 3: Chapter 3 -- Structures and strategies for state space search

 

Week 4 & 5: Chapter 15 -- An introduction to Prolog

·        Programming project 1

 

Week 6: Chapter 4 – Heuristic search

·        Written assignment 2

 

Week 7: Chapter 5 – Stochastic Methods

 

Week 8: Chapter 6 – Architectures for AI problem solving

·        Programming project 2

 

Week 9: Midterm Test

 

Week 10: Chapter 7 – Introduction to AI representation schemes

·        Written assignment 3

 

Week 11: Chapter 8 – Rule-based, case-based, and model-based systems

 

Week 12: Chapter 9 – Reasoning in situations of uncertainty

·        Programming project 3

 

Week 13: Chapter 13 – Automated reasoning

·        Written assignment 4

 

Week 14: Chapter 10 – Machine learning: Symbol-based

 

Week 15: Chapter 11 – Machine learning: Connectionist

 

Week 16: Final Examination: December 12, Tuesday, 2006 at 1:00pm to 3:00pm