Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to two-valued logic. Moreover, there are few if any published reports of expert systems in real-world use that reason about uncertainty using fuzzy logic. It appears that the limitations of fuzzy logic have not been detrimental in control applications because current fuzzy controllers are far simpler than other knowledge-based systems. In the future, the technical limitations of fuzzy logic can be expected to become important in practice, and work on fuzzy controllers will also encounter several problems of scale already known for other knowledge-based systems. 1

In this paper, it is shown that fuzzy sets and possibility theory provide an homogeneous framework for the representation of both imprecise/uncertain information and soft queries with a flexible interpretation. Incompletely known information as well as flexible query handling capabilities are expected to extend the range of applications for future database management systems. The term fuzzy databases which is extensively used in the specialized literature covers several different meanings which are reviewed. A special emphasis is put on flexible queries addressed to regular databases. Such queries enables the user to easily express preferences among more or less admissible attribute values. Several approaches for introducing flexibility, including fuzzy sets, are compared. A query language based on SQL is outlined and some issues related to query processing are discussed. In addition, possibility theory proves to be useful for representing imperfectly known data and soft constraints. P...

Abstract-Generating relevant and manageable search results is of great interest in database queries and information retrieval nowadays. This paper proposes an approach to measuring relation compactness and to generating a compact set of query outcomes under a fuzzy relational data model. The approach has desirable properties that (1) the resultant compact set is unique and contains mutually distinct tuples at the specified degree, and (2) the resultant compact set is information-equivalent to the collection of original query outcomes. Moreover, it is deemed appealing that the approach provides a way to obtain query outcomes of different size in a flexible manner according to the specified degree of compactness or the preferred number of tuples by users. Keywords—Tuple closeness, Fuzzy databases and queries, Relation compactness, Information equivalence, Web search 1

Abstract—In this paper we are using intuitionistic fuzzy approach to minimize search domain in an accidental case where the data collected for investigation is intuitionistic fuzzy in nature. To handle these types of imprecise information we use intuitionistic fuzzy tolerance relation and translate intuitionistic fuzzy query to reach to the conclusion. Here we present an example of vehicle hit and run case where the accused had fled the accident spot within seconds leaving no clue behind. Keywords- fuzzy sets, Intuitionistic fuzzy relation, Intuitionistic fuzzy database, Intuitionistic fuzzy tolerance. I.

The responses to my article provide an that I have is whether the distinction is re- knowledge becomes implicit background exceptionally wide range of perspectives ally well defined. On the one hand, there knowledge that must be used tacitly in tunon the current state of research on fuzzy may be multiple types of imprecision and ing the allowed interactions between the logic and its applications. Overall, I find vagueness. Is the domain-independent im- items of explicit shallow knowledge. To that with most commentators I agree more precision involved in “around 1.80m ” the quote Garcia, “The dogma of generality than I disagree. I shall try here to steer a same as the human-specific imprecision versus efficiency strikes again, and knowlmiddle course between simply repeating involved in “tall”? On the other hand, it edge engineering and machine learning are points of agreement and narrowly counter- may be possible to model some types of not exempted.” ing points of disagreement. imprecision probabilistically. For example, the degree of truth of the assertion “ 1.SOm Fuzzy logic in expert systems. Only three The foundations of fuzzy logic. Some is tall ” might be modeled as the probability of the responses give references in an atcommentators take a more extreme posi- that an individual with height 1.80m would tempt to dispute the claim that there are tion than I do concerning the coherence of be labeled as tall given incomplete knowl- very few deployed expert systems that acfuzzy logic. I do not agree with Attikiouzel edge, that is, given no other information on tually use fuzzy logic as their principal