An Introduction to Artificial Intelligence

CS 427 / 527

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Instructor: Prof. Lydia Tapia


Office: 349E
Office Hours: Tues. 2:00-4:00 and Wed. 2:00-3:00; other times by appointment
Office Phone: 505-277-0858

TA: Pravallika Devineni
Office: 126
Office Hours: Monday 3:00-5:00pm and Thursday 11:00am-12:00pm; other times by appointment

Course URL:

Lecture: Tuesday, Thursday 12:30-1:45 PM, SARA RAYNOLDS HALL (SARAR) 101

Textbook: Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th Edition), George F. Luger, Addison-Wesley Pearson, 2009.

(optional) AI Algorithms, Data Structures and Idioms in Prolog, Lisp, and Java, George F. Luger and William A. Stubblefield, Pearson Education, 2009.

Course Content and Tentative Schedule: The course will cover the following topics.

Week of Topic Reading
8/21 AI, its roots and scope Ch 1
8/28 The Predicate Calculus Ch 2
9/4 Structures and strategies for state space search Ch 3
9/11 Heuristic search Ch 4
9/18 Probabilistic Methods in AI Ch 5
9/25 Architectures for AI problem solving Ch 6
Midterm 1
10/2 Prolog N/A
10/9 Intro. AI representational Schemes Ch 7
10/16 Representation & knowledge-based systems Ch 8
10/23 Representation & knowledge-based systems Ch 8
10/30 Reasoning in uncertain situations Ch 9
11/6 Building a rule based expert system in Prolog N/A
11/13 Building a rule based expert system in Prolog N/A
11/20 Advanced topic: Robotic Intelligence N/A
11/27 Advanced topic: Robotic Intelligence N/A
11/4 Course Summary and review Ch 16
Midterm 2

Assignments and Grading: All assignments will be announced in class and posted on the course web page. If you miss class for any reason, it is your responsibility to find out what assignments you missed.

Your grade will be based on four components:
No late assignments will be accepted. There will be no make-up exams except for university-excused absences. Please discuss unusual circumstances in advance with the instructor.

Course grades will be assigned according to this scale:
A for 90% or above of the total points,
B for 80 to 89%,
C for 70 to 79%,
D for 60 to 69%,
and F for less than 60%.

Academic Integrity: For everyone's benefit, students should uphold the guidelines in the University of New Mexico Student Code of Conduct.

For the assignments in this class, discussion of concepts with others is encouraged, but all assignments must be done on your own, unless otherwise instructed. If you use any source other than the text, reference it/him/her, whether it be a person, a book, a solution set, a web page or whatever. You MUST write up the solutions in your own words. Copying is strictly forbidden.

Americans with Disabilities Act (ADA) Policy Statement: The Americans with Disabilities Act (ADA) is a federal antidiscrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact the Department of Student Affairs, Accessibility Resource Center in Mesa Vista Hall, Rm. 2021.

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  • Homework assignment one: Textbook Chapter 1. Read Chapter One and turn in a one page (single spaced) typed writeup on 3 things you learned. Chapter 1 is available for download in Useful Links Due: 12:30pm on Thursday, Aug 23rd.
  • Homework assignment two: (1) Textbook page 78, #6. (2) Textbook page 78, #10. (3) Textbook page 78, #12. Due: 12:30pm on Tuesday, Sept 4th. Extended to Thurs, Sept 6th.
  • Homework assignment three:
    Both CS427/CS527: (1) Determine whether goal-driven or data-driven search would be preferable for solution of the following problems. Textbook page 122, Chapter 3, #7.
    CS427: Pseudo code a general search algorithm. Then, provide wrappers to it to call BFS and DFS.
    CS527: Recall the 4 rubrics for evaluating search. For each of these rubrics, give analysis to justify their values for BFS and DFS.
    Due: 12:30pm on Tuesday, Sept 18th.
  • Homework assignment four (Chapter 4):
    (1) Textbook page 162, #6, Heuristic Comparision. (2) Textbook page 163, #13, Minimax execution. (3) Textbook page 164, #14, Alpha-beta pruning. Due: 12:30pm on Thursday, Sept. 27th,
  • Homework assignment five (Chapter 5):
    (1) Textbook page 192, #14, Manufacturing a product. (2) Textbook page 192, #16, Automobile insurance company. (3) Textbook page 192, #17, Prisoner probabilities. Due: 12:30pm on Thursday, Oct. 4th.

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    There will be 2-3 programming tasks. Possible assignments include:

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    Students are expected to write a paper on a topic on AI during the course of this class. Papers must be typed and emailed as a PDF to the Instructor and the TA. This paper can cover a specific AI methodology or a specific problem in AI. The subject of the paper is up to the student. However, it is suggested that students get pre-approval of their topic by the course instructor. This can be done by sending an email to the instructor with the subject line: "CS 427 -- Paper Topic Pre-approval" or "CS 527 -- Paper Topic Pre-approval"

    Specific details for CS 427:

    Specific details for CS 527: Possible paper topics include:

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    Useful Links

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    Announcements Syllabus Homework Projects Paper Useful Links Slides