CS440 Fall 1998: Introduction to Artificial Intelligence

What's New

Description:

The course objectives are to learn symbolic computation using Common Lisp and Prolog, to practice techniques for programming AI applications, and to introduce the fundamental theories, algorithms and representational structures underlying Artificial Intelligence.

Class discussions will range from Lisp and Prolog programming fundamentals to philosophical issues in Artificial Intelligence. Lisp and Prolog are used to illustrate basic data structures and programming techniques in AI. Common Lisp and Prolog programs implementing problem-solving search methods, logical reasoning techniques, rule-based systems, semantic nets, and frames will be studied and modified. We will also briefly look at machine learning and neural networks. Other topics will be covered as time permits.

The class is structured around a lecture format; however, class discussions, questions and participation are strongly encouraged.

Logistics

When:Tues,Thur 12:30-1:45PM
Where:332 Gifford
Instructor:Whitley
970-491-5373
whitley@cs.colostate.edu
Office hours: Mon 2:30-3:30, Thurs 3:00-4:00
USC 227
GTA: Dian Hansen
hansend@cs.colostate.edu
HOURS Mon 5-7, Wed 2-4 (South Lab)

Course Requirements

Textbook:

Artificial Intelligence: A Modern Approach by Russell and Norvig.

Pre-requisites:

CS253 and CS301. Students are expected to have introductory knowledge of Common Lisp and knowledge of discrete structures.

Software Requirements:

Interpreters for Common Lisp and Prolog. Any Lisp implementation should follow the Common Lisp standard. At CSU, we will be using Allegro Common Lisp and SBProlog, both public-domain environments. These can be obtained by off-site students for a number of machine types. Other implementations may be acceptable as well; check with the instructor.

Grading:

The course requires demonstration of student's grasp of the concepts through exams, homework assignments and a paper.

Assignments (5 to 8)21%
Paper 20%
Exam 1 25%
Exam 2 25%
quiz 9%
Optional Final Exam25%

Homework:
Five to eight homework assignments will be assigned during the semester. Many of the assignments will be small programming projects. A few assignments may involve writing. At least one assignment will involve a larger programming project.
Paper:
A research paper is required for the class. Examples of topics include: planning, computer vision, natural language processing, neural networks, machine learning, genetic algorithms, computer chess, etc. At least 10 references are required. The paper must be formated (e.g. latex, word). The paper should be 8 to 10 pages using 12 point font. The paper should be evaluative in nature. How does the technology work, what are its strengths and weaknesses, and what kind of applications does it have? Copying text (e.g. sentences, paragraphs or minor variations on the original text) from published works or from other research papers will result in a zero on the paper.

Examinations:
Two exams and one quiz will be given. Note that the exams, quiz, assignments and paper total 100 points. The final is optional. If you are not happy with your grade, your pre-final points will be scaled to 75 percent, and the final will be worth 25 percent.

Logistics Related to Grading:

Each assignment must be submitted at the beginning of class on the given deadline for that assignment; late period for assignments will be the start of the next following class and will incur a penalty of 10%. Assignments turned in after that time receive no credit. If you miss one exam, the final will substitute for the missed exam. If you miss a second exam, you will receive a zero for the exam. No make-up exams will be given.

I encourage you to talk with other students about your assignments and questions, but make sure you write your own programs and assignments. You may not copy another student's program (either with or without their knowledge) or write code for them. Copying of programs will result in a grade of zero for ALL assignments. Please read the departmental policy statement regarding incompletes, cheating, and class attendance. This policy statement is in the file student info. We will follow the guidelines outlined in these documents.

Course Topics:

DateTopicReadings
8/24,26Class requirements. Introduction to AI. Agents.
8/31-9/2 Search basics Ch.1,2
9/7Search basics, Common Lisp Ch.3
9/9,14 Common Lisp and AI programming
9/16Common Lisp and AI programming
9/21** REVIEW
9/23** EXAM 1.
9/28,30Search - StrategiesCh. 4
10/5,7 Genetic Algs-Genetic Programming
10/12,14Feedforward Neural NetworksCh.19
10/19Hopfield Neural NetworksCh 10. 316-327
10/21Propositional CalculusCh 6. 157-174
10/26,28Predicate Calculus and InferencingCh.7 185-201
11/2,4Resolution and Refutation, PrologCh.9
11/9,11Prolog Programming
11/16,18REVIEW and EXAM
11/23,25THANKSGIVING
11/30, 12/2Machine LearningCh.18
12/7Decision Trees/QUIZCh.18:525-544
12/9Review and Wrap-up
12/15 1:30-3:30PM Final Exam

Resources Available: