TR 2:45-4 Unix Lab (NLH 214)
Gary Parker
NLH 216 (ext. 5208)
parker@conncoll.edu
Office Hours: NLH 216 4-5pm Tuesday and Wednesday
Jim O'Connor (joconno2@conncoll.edu)
Manan Isak (misak@conncoll.edu)
Bill Tran (dtran5@conncoll.edu)
Text:Artificial Intelligence (any edition) by Michael Negnevitsky. Additional readings will be assigned in class.
Software:The software used for learning programs will be Xpilot-AI, available at http://www.xpilot-ai.org/.
Web Page:http://cs.conncoll.edu/parker/com407
Computational methods that display aspects of intelligent behavior observed in humans. Topics may include fuzzy logic, an alternative to traditional formal logic; artificial neural networks, networks of simple arithmetic computing elements that abstractly simulate neurons; and genetic algorithms, based on the laws of survival of the fittest and natural selection. How these methods deal with vague, imprecise, and uncertain knowledge; learn from experience; self-organize; and adapt their behavior in response to changing conditions to solve real world problems. Utilization of projects and the discussion of technical papers to cover methods of computational intelligence and their use.
Prerequisite: Course 304, Course 310, Course 316, or Course 320.
This course is a study of fuzzy logic, artificial neural networks, and evolutionary computation. Fuzzy logic is an alternative to traditional formal logic that allows one to deal with vague, imprecise, and uncertain knowledge. Artificial neural networks are networks of simple arithmetic computing elements that abstractly simulate neurons. Evolutionary computation uses the observed laws of survival of the fittest and natural selection to find solutions to complex problems. These techniques are complementary to one another and allow for the formation of solutions with features that are often assumed to be signs of intelligence.
The class will be taught in two parts. In the first, we will concentrate on learning the basics of the three components of computational intelligence. This will be done through readings from the text, lectures, discussions, and homework assignments. The second part will take us more into the advanced issues of fuzzy logic, neural networks, and evolutionary computation. This will be done through the reading / presentation / discussion of technical papers and through the completion of a final project. The project will start with research in your area of interest to determine what you want to do and what work has been done in the past. It will continue with a written proposal. After approval of your proposal, you will start doing the research, making periodic reports on your progress. Finally, you will produce a paper and present it at the end of the semester.
Lead Class Discussion / Presentation |
10% |
Class Participation |
20% |
Homework |
35% |
Final Project |
35% |
Lead Class Discussion / Presentation:You lead the class in discussion on one of the assigned readings given to the entire class and present any pertinent points not covered during the discussion. You will be graded on how you lead the discussion and how well you cover the material. You will also present a paper you find that pertains to your final project.
Class Participation:This portion of the grade includes your participation and readiness to discuss assigned readings during class.
Homework:Homework will be assigned during the first part of the course. In some cases it will be a problem to solve and in other cases it will be a programming assignment. Homework can be done individually or in a team (up to three).
Final Project:The project will be on a topic of your choice. The grade includes the proposal, periodic progress reports, a web site or paper, and a final presentation / demonstration. The project can be done individually or in a team (up to three).
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