General motivations. Computers have been actively applied in many areas of science and engineering. For example, to predict weather, we simulate how the weather parameters change -- by programming the corresponding numerical models.
However, it is well known that models and equations are only a part of knowledge. For example, as every person who works in applications knows, often mathematically correct solutions to these equations make no physical sense. We need an expert -- e.g., a professional geologist -- to separate physically meaningful from physically senseless solutions.
A medical doctor uses a large amount of data to make a decision, but in most cases, we cannot replace the doctor with an algorithm: there is an important part of the doctor's expert knowledge that cannot be easily described by an algorithm.
There is no need to invoke experts to explain the need for expert knowledge: most people can drive cars, but it is very difficult to design an algorithm for driving cars.
The difficulty is that we can easily explain our rules for driving -- just like a medical doctor can often explain her reasons for making a good diagnostic decision -- but this explanation will be often formulated by using words from natural language, like "close" (the car in front of me was too close, so ...), or "high" (the patient's fever was high, so ...). So, for a computer program to use this knowledge, we must be able to describe such words in terms that a computer can understand and process - i.e., in terms of numbers.
Fuzzy logic is a general name for different techniques that transform knowledge described by words from natural language into a numerical algorithmic form. Some of these techniques are similar to probabilistic and statistical techniques traditionally used in science and engineering, some use more sophisticated approaches such as multi-valued logic -- with additional truth values like "probably true" or "very possibly false").
These are the techniques that we will study in this course.
Why applications to geosciences and to environmental sciences. Geosciences and environmental science are two examples of practically important research areas in which, in principle, we know the equations but the data is sparse and thus, we need to supplement it with expert knowledge.
Why applications to information assurance. Information assurance -- securing computer communications, securing integrity and confidentiality of the data, etc. -- are important but extremely difficult areas of research, difficult because, as everyone understands, good protection algorithms help, but we still need the expertise of a skilled system administrator.
Information assurance is one of the main research and education directions of our department:
Why study fuzzy techniques now. On March 18-20, 2011, one of the major international conferences on fuzzy techniques, an annual conference organized by the North American Fuzzy Information Processing Society, will be held in El Paso. This conference is usually sponsored by IEEE, as a result of which Proceedings of this conference become part of the widely used IEEE IExplore publications database. This is an excellent opportunity to present our results.
We expect that many students taking this class will not only learn the new material, they will also participate in ongoing research projects -- and several of these projects will eventually hopefully lead to co-authorship of papers published in the proceedings of this conference. The plan is that graduate students who are already working in these directions will lead groups of interested students from the class who volunteer to help with their projects.
Handouts instead of a textbook. Our emphasis in this course are on new applications of fuzzy techniques, specially on application to information assurance, geosciences, and environmental sciences. While there are good textbooks on fuzzy techniques in general, the applications in which we are interested are so new that they have not yet been adequately reflected in these textbooks. As a result, in this class, instead of a textbook, we will use research papers and other handouts.
What we expect from the students taking this class. As we have mentioned, the main purpose of fuzzy techniques is to use numerical methods to process expert information, and that many ways of generate the corresponding numbers are similar to the methods used in probability and statistics. In view of this, for taking this class, it is helpful to know the basics of numerical methods and the basics of probabilities and statistics. While having the corresponding class as a pre-requisite is desirable, it is not required: students who still remember the basics of probability from their Precalculus and Discrete Math courses are also welcome.
Non-CS students. Due to the inter-disciplinary character of potential applications, we welcome not only CS students, but also students from geosciences, environmental sciences, computational sciences, and from other disciplines in which fuzzy techniques can be (and are) useful such as bioinformatics, engineering (especially control), etc. The presence of such students will help application problems.
We realize that some non-CS students may not be as good in programming different fuzzy-related algorithms as our own CS students. With this in mind, we plan to tailor homeworks and other assignments to such students, so that these tailored assignments are more focused on applications and somewhat less on programming.
Plan of study: In the first part of the class:
Tests and Grades: There will be two tests and one final exam. Each topic means home assignments (mainly on the sheets of paper, but some on the real computer). Some of them may be graded. Maximum number of points:
A good project can help but it cannot completely cover possible deficiencies of knowledge as shown on the test and on the homeworks. In general, up to 80 points come from tests and home assignments. So:
Online class evaluations: Close to the end of the class, a website will be made available to the students for online class evaluations. These evaluations are an important part of the class. While remarks that you type in are strictly anonymous, we the faculty will be informed on who submitted their evaluations and who did not. To encourage you to submit your evaluations on time, we will give extra points to those who submit the class evaluations ahead of the deadline.
Standards of Conduct: Students are expected to conduct themselves in a professional and courteous manner, as prescribed by the Standards of Conduct. Students may discuss programming exercises in a general way with other students, but the solutions must be done independently. Similarly, groups may discuss project assignments with other groups, but the solutions must be done by the group itself. Graded work should be unmistakably your own. You may not transcribe or copy a solution taken from another person, book, or other source, e.g., a web page). Professors are required to - and will - report academic dishonesty and any other violation of the Standards of Conduct to the Dean of Students.
Disabilities: If you feel you may have a disability that requires accommodation, contact the Disabled Student Services Office at 747-5148, go to Room 106 E. Union, or e-mail to email@example.com.