Download Course Syllabus - Dr. Randy Ribler

Syllabus for CS 380
Artificial Intelligence
Instructor: Dr. Randy L. Ribler
Office: 103 Hobbs, Phone: 544-8529
Class Web Page: http://lasi.lynchburg.edu/ribler_r/public/cs451
email: [email protected]
Office Hours: MTWF 4:00-6:00, MW 11:00-12:00
Text: Artificial Intelligence – A Guide to Intelligent Systems, by Michael Negnevitsky
Course Description:
This course is a study of the theoretical issues and programming techniques involved in
artificial intelligence. Core topics include search, knowledge representation, and
reasoning. Additional topics may include game theory, planning, understanding, natural
language processing, machine learning, neural networks, genetic algorithms, expert
systems, and real-time systems. Students develop competence in a language widely used
for A.I. programming, typically LISP or Prolog.
Purpose of the Course
This course is designed for software developers who will be utilizing artificial
intelligence in their research and/or applications. It focuses on understanding artificial
intelligence from theoretical, practical, and philosophical points of view. Credit from this
course can be applied as an elective in the computer science major.
Prerequisite: Completion of CS241.
Principal Topics Covered
Definitions of Artificial Intelligence
The Turing Test
State Space Search
Minimax
Alpha-Beta
A*
Expert Systems
Fuzzy Sets
Uncertainty Management
Knowledge Representation
Scripts
Machine Learning
Genetic Algorithms and Evolutionary Computation
Neural Networks
Rule Induction
Natural Language Understanding
Knowledge Engineering and Data Mining
Artificial Life
Prolog and Logic Programming
Course objectives/Learning Outcomes
Students will understand what constitutes the field of Artificial Intelligence and will be
able to discuss its practical application, promise as a research field, and social
implications.
Students will be able to critique the Turing Test and explain its significance.
Students will be able to explain and implement search algorithms including minimax,
alpha-beta, and A*.
Students will be able to implement an expert system using one or more expert system
development tools.
Students will be able to apply fuzzy set theory to the implementation of expert systems.
Students will be able to apply Bayesian statistics to provide measures of uncertainty.
Students will be able to distinguish between problems that are well suited to the
application of AI techniques and those that are not.
Students will be able to implement a simple genetic algorithm and use it to solve an
appropriate problem.
Student will be able to train neural networks to solve an appropriate problem.
Student will understand rule-induction and will be able to construct or apply programs
that generate rules from sample data.
Students will understand the principal obstacles to natural language understanding, some
basic parsing algorithms, and the role that knowledge representation has in the process.
Students will be able to write simple Prolog programs and will understand unification and
the motivation behind logic programming.
Grading
Tests and Quizzes (45%):
Test #1 (10%)
Test #2 (10%)
Quizzes (10%)
Final (15%)*
Programs/Exercises (45%):
3d Tic-Tac-Toe (alpha-beta search) (15%)*
5-7 Programs and Exercises (30%)
Class Participation (10%)
Attendance
Punctuality
Participation
Group Participation (30%)
Design Reviews/Discussions (15%)
Weekly Reports/ Design Notebook (15%)
*A tournament will be held during the last week of classes to determine the best 3d Tic-Tac-Toe program.
The author of the winning program will receive an automatic “A +” on both the final examination and the
Tic-Tac-Toe program.
Academic Honesty
Internet program source and other published program source may be utilized only with
the instructor's approval. Any existing code, including code written by the student, must
be identified prior to the initiation of the project. Algorithms from any source may be
used, but references must be acknowledged in the body of the program. All instances of
academic dishonesty will be referred to the Honor Board.
Accommodations for Students with Disabilities
The College will make reasonable accommodations for persons with documented
disabilities. Students, who have not already done so, should immediately contact the
Support Services Coordinator located in the Academic Advising Office (extension 8419)
to make arrangements for their accommodations and faculty notification. The instructor
will work with you together with the Support Services Coordinator to produce the best
learning environment possible.