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COURSE DESCRIPTION
Department and Course
Number
Course
Title
CIS 479
Artificial Intelligence
Course
Coordinator
Total
Credits
Bruce R. Maxim
3
Current Catalog Description
This course introduces students to basic concepts and methods of artificial intelligence
from a computer science perspective. Emphasis of the course will be on the selection
of data representations and algorithms useful in the design and implementation of
intelligent systems. The course will contain an overview of one AI language and some
discussion of important applications of artificial intelligence methodology.
Textbook
Luger, G. Artificial Intelligence (5th Edition), 2005.
References
Chopin, B. Artificial Intelligence Illuminated, 2004.
Winston, P. H. and Horn, B. K. P. Lisp (3rd Edition), 1989.
Course Goals
This course is intended to provide an overview of the problems and methods studied in
the field of artificial intelligence. The focus of the course will be on the study of
methods of knowledge representation, data structures, and algorithms useful to the
development of intelligent programs. The course will also include discussion of
important applications of AI methodology.
Prerequisites by Topic
1. Discrete mathematics is assumed to have been taken before this class.
2. Knowledge of at least one modem programming language.
3. Ability to write medium sized programs.
4. Knowledge of data structures (linked-lists, trees, graphs).
5. Knowledge of recursive algorithms.
6. Knowledge of searching and sorting algorithms.
7. Ability to perform algorithm analysis.
8. Knowledge of truth functional logic.
9. Some knowledge of elementary probability theory.
10. Some understanding of grammars.
Major Topics Covered in the Course
1. LISP programming language (9 hours)
2. Heuristic Search Algorithms (3 hours)
3. Game Playing (3 hours)
4. Automated Problem Solving (3 hours)
5. Knowledge Representation: Semantic Nets, Frames, Scripts (4 hours)
6. Predicate Logic (2 hours)
7. Production Systems and Knowledge Engineering Tools (6 hours)
8. Uncertainty and Statistical Reasoning (3 hours)
9. Planning Systems (2 hours)
10. Machine Learning (3 hours)
11. Neural Networks (3 hours)
12. Constraint Satisfaction (3 hours)
13. Parallel and Distributed AI (3 hours)
14. Exams (5 hours)
Laboratory projects (specify number of weeks on each)
1. Write a series of Lisp functions from a simple application area. (2 weeks)
2. Write game playing program in which the computer opponent uses mini-max with
alpha-beta pruning. (5 weeks)
3. Perform the knowledge engineering for a classification or diagnostic task and used an
expert system shell to implement the system (4 weeks)
4. Conduct experiments using an autonomous agent toolkit. (2 weeks)
Estimate CSAB Category Content
CORE
ADVANCED
CORE
1.0
ADVANCED
0.5
Computer Organization and
Architecture
Data Structures
1.0
Algorithms
Software Design
0.5
Concepts of Programming
Languages
Oral and Written Communications
Every student is required to submit at least 4 written reports (not including exams,
tests, quizzes, or commented programs) of typically 10 pages and to make 1 oral
presentations of typically 10 minutes duration.
Social and Ethical Issues
1. Human risks and the development of knowledge-based software - exam questions
Theoretical Content
1. Heuristics and Algorithms (6 hours)
2. Programming Language Paradigms (4 hours)
3. Algorithm Complexity (2 hours)
4. Knowledge Representation (4 hours)
5. Machine Learning Models (2 hours)
6. Automated Problem Solving Models (2 hours)
7. Non-Deterministic Reasoning (4 hours)
Problem Analysis
1. Identify a domain expert for some classification or diagnostic task and construct a
rule-based representation of this knowledge. (2 weeks)
2. Identify a source of training facts for some classification, diagnostic, or predictive
task. (2 weeks)
Solution Design
1. Design a game playing program which uses the mini-max algorithm with alpha-beta
pruning as the basis of generating the computer's next move. (5 weeks)
2. Identify a domain expert for some classification or diagnostic task and construct a
rule-based system that can solve representative problems in this domain. (4 weeks)