Special Session at HIS'03

Overview:

1. Rough Sets: Theory and Applications
2. Intelligent and Expert Systems in Education
3. Intelligent Agent Enabled E-commerce on Heterogeneous Devices


1. Rough Sets: Theory and Applications
Author(s): +Jerzy W. Grzymala-Busse and ++Lech Polkowski
+Department of Electrical Engineering and Computer Science
University of Kansas
1460 Jayhawk Blvd.
Lawrence, KS 66045-7523
USA
E-mail: jerzy@ku.edu
Web: http://lightning.eecs.ku.edu/index.html
++Polish-Japanese Institute of Information Technology
Koszykowa 86
02-008 Warsaw
Poland
E-mail: polkow@pjwstk.waw.pl

Abstract. Rough set theory, originated by Z. Pawlak in 1982, is a formal
mathematical theory modeling knowledge in terms of equivalence
relations. The main advantage of rough set theory is that it does
not need any preliminary or additional information about data
(like probability in probability theory, grade of membership in
fuzzy set theory, etc.). Rough set theory was applied in a number
of areas, mostly in data mining. In our session we anticipate some
theoretical papers and some papers describing the newest
applications of rough set theory to data mining.

Papers:
1) Approximated Measures in Construction of Decision Trees from Large Databases, H. S. Nguyen and S. H. Nguyen
2) Approximating Monotone Concepts, J. Saquer and J.S. Deogun
3) Diagnosis of Melanoma Based on Data Mining and ABCD Formulars, S. Bajcar, J.W. Grzymala-Busse, W.J. Grzymala-Busse and Z.S. Hippe
4) Rough-Fuzzy-Neurocomputing Based on Rough Mereological Calcus of Granules, L. Polkowski and M. Semaniuk-Polkowska
5) Hybridized Rough Set Framework for Classification: An Experimental View, S. Minz and R. Jain


2. Intelligent and Expert Systems in Education
Author(s): Z. J. Pudlowski and Arun S. Patil
Room 208, UICEE
Monash University, Building 70, Clayton
Melbourne, Australia
VIC-3800
Tel.: +61-3-99054090
E-mail: arun.patil@eng.monash.edu.au

Abstract. Numerous research and studies show that the Web-based intelligent learning is becoming more effective. Due to the rapid growth of the use of computers in education, as well as the introduction of the World Wide Web (WWW), a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The principle of AI made computers more useful, as well as intelligent, in order to utilise them in all the fields of human life. The application of AI principles is the next advanced step to a Web-based ITS, which began in the 1970s and 1980s. Since then, the influence of AI on software technology has considerably increased. As a result, the use of AI techniques in teaching/learning, such as expert systems, simulations and robotics, etc, has become a major factor in the development of Web-based intelligent authoring systems. AI is an advanced scientific technology that is used for efficient computer-based problem-solving techniques in various disciplines.
The important contribution of AI in computer-based education is to provide knowledge-based
access to resources. Wilson and Welsh (1991) divided AI into three broad areas where knowledgebased systems or expert systems can have important implications for education and training. The history of computerised educational measurement system shows that each generation of educational measurement has shown an increased use of AI and expert systems approaches in order to improve educational measurement activities. The four important generations highlighted by Olsen (1991) are:

• Computerised testing;
• Computerised adaptive testing;
• Continuous measurement;
• Intelligent measurement.

Although educationalists are fascinated by the applications of AI techniques in various courseware
developments, McArthur et al (1993) claim that the application of AI in education has somewhat
diversified and the approaches are more fractured.
The proposed session on intelligent and expert systems in education will create important
opportunities to the educational developers and experts so as to discuss important challenges, issues
and developments of AI application in teaching/learning for various levels of courses in classroom
based as well as Web-based educational system.

REFERENCES:
1. McArthur, D., Lewis, M.W. and Bishay, M., The Roles of Artificial Intelligence in Education:
Current Progress and Future Prospects. Santa Monica: RAND (1993),
http://www.rand.org/education/mcarthur/Papers/roleab.html
2. Olsen, J.B., The Four Generations of Computerised Testing: toward Increased Use of AI and
Expert Systems. In: Expert Systems and Intelligent Computer-Aided Instructions. Englewood
Cliffs: Educational Technology Publications, 66-71 (1991).
3. Wilson, B.G. and Welsh, J.R., Small Knowledge-Based Systems in Education and Training:
Something New under the Sun. In: Expert Systems and Intelligent Computer-Aided Instruction,
Vol. 2. Englewood Cliffs: Educational Technology Publications (1991).


3. Intelligent Agent Enabled E-commerce on Heterogeneous Devices
Author(s): Raj Dasgupta
Computer Science Department
University of Nebraska at Omaha
USA
Tel.: (402) 554-4966
Fax: (402) 554-2384
E-mail: pdasgupta@mail.unomaha.edu