AI Applications

SA09.1 Intelligent Data Mining Using Object-Oriented Paradigm

• Davinder K. Malhotra; Philadelphia College, Sch. House Ln. & Henry Ave., Philadelphia, PA 19144; malhotrad@philacol.edu
• Rashmi Malhotra; St. Joseph's University, Dept. of MIS, 5600 City Ave., Philadelphia, PA 19131;

Although many different intelligent techniques, i.e., neural network methods, GAs and neuro-fuzzy models, etc., have been applied for data mining and data warehousing, knowledge-based systems have not been very extensively applied to the DSS data analysis. We illustrate the use of artificially intelligent knowledge-based methods in retrieving information from the morningstar's mutual fund database.

SA09.2 A Geometric Model for Computational Music Analysis

• Elaine Chew; MIT, 158 Webster Ave., Cambridge, MA 02141; eniale@mit.edu,, http://web.mit.edu/eniale

Enjoyment of the arts is a defining feature of an enhanced quality of life. The quest for deeper understanding and appreciation naturally follows. Computational tonal analysis is a difficult problem which has been tackled with limited success in music cognition. My new approach utilizes a geometric model which incorporates functional pitch relations.

SA09.3 A Generalized Neural Network Approach to Chemical Oxidation Process Modeling

• Sarah S. Lam; Binghamton University, Dept. of Systems Sci. & IE, Binghamton, NY 13902; sslst7@pitt.edu
• Alice E. Smith; Auburn University, ISE Dept., 207 Dunston Hall, Auburn, AL 36832; aesmith@eng.auburn.edu

An approach that considers multiple feed gas types and reactor types for a chemical oxidation process is presented. In contrast to a more traditional approach, quadratic response surface modeling, our approach requires the construction of one generalized model, which can be used for optimizing the process...

SA09.4 Sensitivity Tests on a Set of Learning Classifier Systems Trained to Play the Iterated Prisoner's Dilemma Problem

• Kenneth S. Sims; Cumberland College, 7656 College Station Dr., Williamsburg, KY 40769-1387; kenpc@cc.cumber.edu
• Ram Pakath; University of Kentucky, DSIS, Sch. of Mgmt., 425D Gatton B&E Bldg., Lexington, KY 40506-0034; pakath@ukc.uky.edu

We extend prior explorations that use LCS-based agents to generate superior IPD game-playing strategies against opponents playing human-proposed strategies. We provide a 'sensitivity analysis' of the LCS by observing its reactions to alterations in some of its parameters and to some opponent parameters.