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— 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- facts or rules, between selected objects of discourse. The use of fuzzy sets, Intuitionistic fuzzy relation, Intuitionistic fuzzy database, Intuitionistic fuzzy tolerance. I. I NTRODUCTION A fuzzy database utilizes the fuzzy logic, where fuzzy relational schemas are used to represent imprecise data. But in some application fuzzy sets are found unsatisfactory in capturing the information that includes some hesitation degree. So to deal with such application Intuitionistic fuzzy sets are used. Intuitionistic fuzzy set theory (IFS) is a significant generalization of fuzzy set. It was introduced by Krassimir Atanassov [1-4] in year 1983. Intuitionistic fuzzy sets can be useful in situations when description of a problem by a (fuzzy) linguistic variable, given in terms of a membership function only, seems insufficient to give best result. For example, in decision making problems, particularly in the case of medical diagnosis, sales analysis, new product marketing, financial services, etc.[5-6] there is a fair chance of the existence of a non-null hesitation part at each moment of evaluation of an unknown object. When the data become intuitionistic fuzzy in nature we need Intuitionistic Fuzzy Databases (IFDB) as the fuzzy database is not appropriate for handling intuitionistic fuzzy data. IFDB was introduced by Biswas, De. and Roy[7-12]. II. BACKGROUND DETAILS Intuitionistic fuzzy set (IFS), developed by Atanassov is a powerful tool to deal with vagueness. A prominent characteristic of IFS is that it assigns to each element a membership degree and a non-membership degree, and thus, the IFS constitutes an extension of Zadeh’s fuzzy set, which only assigns to each element a membership degree. Recently various applications of IFS to artificial intelligence have appeared - intuitionistic fuzzy expert systems, intuitionistic fuzzy neural networks, intuitionistic fuzzy decision making, intuitionistic fuzzy machine learning, intuitionistic fuzzy semantic representations. The strength of relations as information models in knowledge based system derives from their fundamental ability to describe observed or predicted “connections", expressed as fuzzy relations originated from the observation that real life objects can be related to each other to a certain degree (just like elements can belong to a fuzzy set to a certain degree); in this sense they are able to model vagueness. They are still intolerant of uncertainty, however, since there is no means of attributing reliability or confidence information to the membership degrees. Various frameworks have been developed to deal with this imperfection, amongst others. Here we will concentrate on the IF approach. A possible semantics for an IF relations taking inspiration from classical possibility theory was presented in [13-14]. Basically, the idea is to treat an IF relation as an elastic restriction that allows us to discriminate between the more or less plausible values for a variable. For instance, a statement like “John is old" does not allow us to infer John's exact age, yet provides some support in favor of the older ages (allowing that those ages are, to a given extent, possible for him), as well as negative evidence against the younger ones (expressing some certainty or necessity that those ages can't in fact be his). We model this observation by indicating how much the original condition “John is old" needs to be stretched in order for John's age to assume this particular value: we assign two separate [0, 1] valued degrees μ

Applying Intuitionistic Fuzzy Approach to Reduce Search Domain in an Accidental Case