A Model of the Distributed Constraint Satisfaction Problem and an Algorithm for Configuration Desing
Leonid Sheremetov, Alexander V. Smirnov · Redalyc (Universidad Autónoma del Estado de México) · 1997
IN THIS PAPER WE DISCUSS OUR APPROACH TO LEANING CLASSIFICATION RULES FROM DATA. WE SKETCH OUT TWO MODULES OF OUR ARCHITECTURE, NAMELY LINNEO AND GAR.LINNEO, WHICH IS A KNOWLWDGE ACQUISITION TOOL FOR ILL - STRUCTURED DOMAINS AUTOMATICALLY GENERATING CLASSES FROM EXAMPLES THAT INCREMENTALLY WORKS WHIT AN UNSUPERVISED STRATEGY. LINNEO'S OUTPUT, A REPRESENTATION OF THE CONCEPTUAL STRUCTURE OF THE DOMAIN IN TERMS OF CLASSES, IN THE INPUT TO GAR THAT IS USED TO GENERATE A SET OF CLASSIFICATION RULES FOR THE ORIGINAL TRAINING SET. GAR CAN GENERATE BOTH CONJUCTIVE AND DISJUNCTIVE RULES. HEREIN WE PRESENT AN APPLICATION OF THESE TECHNIQUES TO DATA OBTAINED FROM A REAL WASTEWATER TREATMENT PLANT IN ORDER TO HELP THE CONSTRUCTION OF A RULE BASE. THIS RULE WILL BE USED FOR A KNWOLEDGE - BASED SYSTEM THAT AIMS TO SUPERVISE THE WHOLE PROCESS.