Results 1  10
of
186
The Concept of a Linguistic Variable and its Application to Approximate Reasoning
 Journal of Information Science
, 1975
"... By a linguistic variable we mean a variable whose values are words or sentences in a natural or artificial language. I:or example, Age is a linguistic variable if its values are linguistic rather than numerical, i.e., young, not young, very young, quite young, old, not very oldand not very young, et ..."
Abstract

Cited by 940 (8 self)
 Add to MetaCart
(Show Context)
By a linguistic variable we mean a variable whose values are words or sentences in a natural or artificial language. I:or example, Age is a linguistic variable if its values are linguistic rather than numerical, i.e., young, not young, very young, quite young, old, not very oldand not very young, etc., rather than 20, 21, 22, 23, In more specific terms, a linguistic variable is characterized by a quintuple (&?, T(z), U, G,M) in which &? is the name of the variable; T(s) is the termset of2, that is, the collection of its linguistic values; U is a universe of discourse; G is a syntactic rule which generates the terms in T(z); and M is a semantic rule which associates with each linguistic value X its meaning, M(X), where M(X) denotes a fuzzy subset of U The meaning of a linguistic value X is characterized by a compatibility function, c: l / + [0, I], which associates with each u in U its compatibility with X. Thus, the COItIpdtibiiity of age 27 with young might be 0.7, while that of 35 might be 0.2. The function of the semantic rule is to relate the compdtibihties of the socalled primary terms in a composite linguistic valuee.g.,.young and old in not very young and not very oldto the compatibility of the composite value. To this end, the hedges
Using Similarity Criteria to Make Issue TradeOffs in Automated Negotiations
 Artificial Intelligence
, 2002
"... Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here ..."
Abstract

Cited by 99 (8 self)
 Add to MetaCart
(Show Context)
Automated negotiation is a key form of interaction in systems that are composed of multiple autonomous agents. The aim of such interactions is to reach agreements through an iterative process of making offers. The content of such proposals are, however, a function of the strategy of the agents. Here we present a strategy called the tradeoff strategy where multiple negotiation decision variables are tradedoff against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer in order to obtain a higher quality service). Such a strategy is commonly known to increase the social welfare of agents. Yet, to date, most computational work in this area has ignored the issue of tradeoffs, instead aiming to increase social welfare through mechanism design. The aim of this paper is to develop a heuristic computational model of the tradeoff strategy and show that it can lead to an increased social welfare of the system. A novel linear algorithm is presented that enables software agents to make tradeoffs for multidimensional goods for the problem of distributed resource allocation.
Towards General Measures of Comparison of Objects
"... We propose a classification of measures enabling to compare fuzzy characterizations of objects, according to their properties and the purpose of their utilization. We establish ..."
Abstract

Cited by 55 (17 self)
 Add to MetaCart
We propose a classification of measures enabling to compare fuzzy characterizations of objects, according to their properties and the purpose of their utilization. We establish
Toward CaseBased Preference Elicitation: Similarity Measures on Preference Structures
 In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence
, 1998
"... While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decisiontheoretic models of ..."
Abstract

Cited by 52 (6 self)
 Add to MetaCart
(Show Context)
While decision theory provides an appealing normative framework for representing rich preference structures, eliciting utility or value functions typically incurs a large cost. For many applications involving interactive systems this overhead precludes the use of formal decisiontheoretic models of preference. Instead of performing elicitation in a vacuum, it would be useful if we could augment directly elicited preferences with some appropriate default information. In this paper we propose a casebased approach to alleviating the preference elicitation bottleneck. Assuming the existence of a population of users from whom we have elicited complete or incomplete preference structures, we propose eliciting the preferences of a new user interactively and incrementally, using the closest existing preference structures as potential defaults. Since a notion of closeness demands a measure of distance among preference structures, this paper takes the first step of studying various distance mea...
Semantic vs. Structural Resemblance of Classes
 SIGMOD Record, special issue on Semantic Issues in Multidatabases
, 1992
"... We present an approach to determine the similarity of classes which utilizes fuzzy and incomplete terminological knowledge together with schema knowledge. We clearly distinguish between semantic similarity determining the degree of resemblance according to real world semantics, and structural corres ..."
Abstract

Cited by 42 (2 self)
 Add to MetaCart
(Show Context)
We present an approach to determine the similarity of classes which utilizes fuzzy and incomplete terminological knowledge together with schema knowledge. We clearly distinguish between semantic similarity determining the degree of resemblance according to real world semantics, and structural correspondence explaining how classes can actually be interrelated. To compute the semantic similarity we introduce the notion of semantic relevance and apply fuzzy set theory to reason about both terminological knowledge and schema knowledge. 1 Introduction The identification of similar or corresponding concepts forms one of the main steps when investigating different world models and relating them to each other. Apart from its long tradition in document retrieval, this issue has also been investigated in more structured frameworks such as schema independent query formulation, e.g., [Mot90], or database integration, where for a survey you may look at [SL90]. As argued in [GPN91], there should b...
GEFRED. A Generalized Model of Fuzzy Relational Data Bases. Ver. 1.1
, 1994
"... In this paper, we present a Fuzzy Relational Databases model whose main characteristics are: the integration of previous models in the same framework, representation capabilities for a wide series of fuzzy information and a coherent and flexible handling of it. This model aims to solve each problem ..."
Abstract

Cited by 30 (11 self)
 Add to MetaCart
In this paper, we present a Fuzzy Relational Databases model whose main characteristics are: the integration of previous models in the same framework, representation capabilities for a wide series of fuzzy information and a coherent and flexible handling of it. This model aims to solve each problem of representation and handling of fuzzy information taking into account its specific nature, and hence it allows the user to choose the comparison operator and the fuzzy compatibility measure to be used in a query. Besides, it permits the user to specify the precision with which the conditions involved in a query are satisfied. Keywords: Fuzzy Relational Database, Database, Fuzzy Sets, Relational Model 1 Introduction The aim of this paper is to present a fuzzy extension to the Databases Relational Model. This extension will try to solve the problems related to the representation and handling of imprecise information. Because of this, we will have to incorporate some new elements into the Rel...
Measurement Of Membership Functions: Theoretical And Empirical Work
, 1995
"... This chapter presents a review of various interpretations of the fuzzy membership function together with ways of obtaining a membership function. We emphasize that different interpretations of the membership function call for different elicitation methods. We try to make this distinction clear u ..."
Abstract

Cited by 22 (1 self)
 Add to MetaCart
This chapter presents a review of various interpretations of the fuzzy membership function together with ways of obtaining a membership function. We emphasize that different interpretations of the membership function call for different elicitation methods. We try to make this distinction clear using techniques from measurement theory.
A SimilarityBased Generalization of Fuzzy Orderings Preserving the Classical Axioms
 Internat. J. Uncertain. Fuzziness KnowledgeBased Systems
, 2000
"... Equivalence relations and orderings are key concepts of mathematics. For both types of relations, formulations within the framework of fuzzy relations have been proposed already in the early days of fuzzy set theory. While similarity (indistinguishability) relations have turned out to be very useful ..."
Abstract

Cited by 19 (13 self)
 Add to MetaCart
(Show Context)
Equivalence relations and orderings are key concepts of mathematics. For both types of relations, formulations within the framework of fuzzy relations have been proposed already in the early days of fuzzy set theory. While similarity (indistinguishability) relations have turned out to be very useful tools, e.g. for the interpretation of fuzzy partitions and fuzzy controllers, the utilization of fuzzy orderings is still lagging far behind, although there are a lot of possible applications, for instance, in fuzzy preference modeling and fuzzy control. The present paper is devoted to this missing link. After a brief motivation, we will critically analyze the existing approach to fuzzy orderings. In the main part, an alternative approach to fuzzy orderings, which also takes the strong connection to gradual similarity into account, is proposed and studied in detail, including several constructions and representation results.
Similarity in Fuzzy Reasoning
 Mathware Soft Comput
, 1995
"... Fuzzy set theory is based on a `fuzzification' of the predicate 2 (element of), the concept of membership degrees is considered as fundamental. In this paper we elucidate the connection between indistinguishability modelled by fuzzy equivalence relations and fuzzy sets. We show that the indist ..."
Abstract

Cited by 19 (2 self)
 Add to MetaCart
(Show Context)
Fuzzy set theory is based on a `fuzzification' of the predicate 2 (element of), the concept of membership degrees is considered as fundamental. In this paper we elucidate the connection between indistinguishability modelled by fuzzy equivalence relations and fuzzy sets. We show that the indistinguishability inherent in fuzzy sets can be computed and that this indistinguishability cannot be overcome in approximate reasoning. For our investigations we generalize from the unit interval as the basis for fuzzy sets to the framework of GLmonoids that can be understood as a generalization of MValgebras. Residuation is a basic concept in GLmonoids and many proofs can be formulated in a simple and clear way using residuation instead of concentrating on special properties of the unit interval. 1 Introduction Fuzzy set theory is based on the idea that many nonmathematical properties cannot be described in terms of crisp sets comprising those elements that fulfill a given property. The...