This paper presents an efficient algorithm to deal with inexact reasoning, where fuzzy production rules are used for knowledge representation and these rules are represented by an augmented fuzzy rule matrix F. The algorithm performs inexact reasoning via repeated transformations of augmented truth state vectors by the transitive closure Q of the augmented fuzzy rule matrix F. Given the truth values of some conditions, the algorithm can perform inexact reasoning to evaluate the truth values of other conditions automatically. The time complexity of the algorithm is 0(k(m+k)2), where m is the number of conditions and k is the number of compound conditions appearing in the antecedent portion of the fuzzy production rules.

A fuzzy Petri net model (FPN) is presented to represent the fuzzy production rule of a rule-based system in which a fuzzy production rule describes the fuzzy relation between two propositions. Based on the fuzzy Petri net model, an efficient algorithm is proposed to perform fuzzy reasoning automatically. It can determine whether an antecedent-consequence relationship exists from proposition d/sub s/ to proposition d/sub j/, where d/sub s/ not=d/sub j/. If the degree of truth of proposition d/sub s/ is given, then the degrees of truth of proposition d/sub j/ can be evaluated. The formal description of the model and the fuzzy reasoning algorithm are shown in detail. The upper bound of the time complexity of the fuzzy reasoning algorithm is O(nm), where n is the number of places and m is the number of transitions. Its execution time is proportional to the number of nodes in a sprouting tree generated by the algorithm only generates necessary reasoning paths from a starting place to a goal place, it can be executed very efficiently.>

The Taiwan government, through the Institute for Information Industry, is undertaking several nationwide projects and developing new products and technology. There are four government-sponsored national programs that clearly indicate the local industry's ability to create software for large-scale computer systems. These projects are the financial information system (FIS), the Taiwan meteorological system, the residential registration system (RRS), and the patient care system. The government, with cooperation and guidance from the private sector, is also addressing the critical need for increased software R&D.>

This paper presents the motivation, history, and idiosyncrasy of a query translation system. Detail of the translation process has also been described.

The concept of a database skeleton which reflects both the user's conception of the real world and the system's understanding of the interrelationships among database entities is described. It consists of a conceptual schema (conceptual graphs) and a relational schema (information graph). With the aid of the database skeleton, fuzzy queries can be translated and disambiguated by analyzing the queries using the conceptual graphs of a database skeleton. The query language XQL is introduced, and the XQL translator is described in some detail.

This paper treats the problem of fuzzy query translation using the concept of database skeleton. An imprecise, or fuzzy, query is represented by a query graph, which can be analyzed for translatability with respect to a given database skeleton. The fuzzy query translation problem is formulated as a problem of converting a partially specified query graph into a completely specified query graph. This graph is then translated into a sequence of data manipulation commands, such as commands in relational algebra. Approaches to optimal fuzzy query translation are also described.