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Exploitation of new and innovative energy alternatives is a key means towards a sustainable energy system. This paper proposes a linguistic energy planning model with computation solely on words as well as considering the policy-maker's preference information. To do so, a probabilistic approach is first proposed to derive the underlying semantic overlapping of linguistic labels from their associated fuzzy membership functions. Second, a satisfactory-oriented choice function is proposed to incorporate the policy-maker's preference information. Third, our model is extended to multicriteria case with linguistic importance weights. One example, borrowed from the literature, is used to show the effectiveness and advantages of our model.

One important issue of uncertain or fuzzy object-oriented models is that uncertain membership degrees of an object to the classes in a class hierarchy may be obtained from different sources while they are actually constrained by the subclass relation. In this paper we present the notion of admissible combination functions and an algorithm to propagate and combine prior uncertain membership degrees on a class hierarchy,which are possibly conflicting, in order to produce a tightly consistent uncertain membership assignment. We assume uncertain membership degrees to be measured by support pairs represented by sub-intervals of [0,1]. The usual probabilistic interval intersection, Dempster-Shafer, and possibilistic combination rules are examined and proved to be admissible ones.

Evaluation for ranking is very useful for users in their decisionmaking process when they want to select some item(s) from a large number of items using their personal preferences. In this paper, we will focus on the evaluation of Japanese traditional crafts, in which product items are assessed according to the so-called Kansei features by means of the semantic differential method. In particular, two decision analysis based evaluation procedures, which take consumer-specified preferences on kansei features of traditional products into consideration, will be discussed and compared.

This paper presents a probabilistic model for linguistic multi-expert decision making (MEDM), which is able to deal with vague concepts in linguistic aggregation and decision-makers’ preference information in choice function. In linguistic aggregation phase, the vagueness of each linguistic judgement is captured by a possibility distribution on a set of linguistic labels. A confidence parameter is also incorporated into the basic model to model experts’ confidence degree. The basic idea of this linguistic aggregation is to transform a possibility distribution into its associated probability distribution. The proposed linguistic aggregation results in a set of labels having a probability distribution. As a choice function, a target-oriented ranking method is proposed, which implies that the decision-maker is satisfactory to choose an alternative as the best if its performance is as at least “good” as his requirements.

The main focus of this paper is for decision analysis from the target-oriented point of view. Firstly, the target achievement computation method is revised, in which the resulting value function can have four shapes: concave, convex, S-shaped, inverse S-shaped. In addition, it is now more and more widely acknowledged that all facets of uncertainty cannot be captured by a single probability distribution. A fuzzy uncertain target-oriented method is also proposed, in which the proportional approach is selected to transform a possibility distribution into its associated probability distribution, and then based on the random target-oriented model, we can obtain the probability of meeting targets. Three types of fuzzy targets, widely used in Bellman-Zadeh paradigm, are selected to illustrate the fuzzy target-oriented model.

If we know the exact consequences of each action, then we can select an action with the largest value of the objective function. In practice, we often only know these values with interval uncertainty. If two intervals intersect, then some people may prefer the alternative corresponding to the first interval, and some prefer thealternative corresponding to the second interval. How can we describe the portion of people who select the first alternative? In this paper, we provide a new theoretical justification for Hurwicz optimism-pessimism approach, and we show how this approach can be used in group decision making.

Decision maker's behavioral aspects play an important role in human decision making, and this was recognized by the award of the 2002 Nobel Prize in Economics to Daniel Kahneman. Target-oriented decision analysis lies in the philosophical root of bounded rationality as well as represents the S -shaped value function. In most studies on target-oriented decision making, monotonic assumptions are given in advance to simplify the problems, e.g., the attribute wealth. However, there are three types of target preferences: "the more the better" (corresponding to benefit target preference), "the less the better" (corresponding to cost target preference), and equal/range targets (too much or too little is not acceptable). Toward this end, two methods have been proposed to model the different types of target preferences: cumulative distribution function (cdf) based method and level set based method. These two methods can both induce four shaped value functions: S -shaped, inverse S -shaped, convex, and concave, which represents decision maker's psychological preference. The main difference between these two methods is that the level set based method induces a steeper value function than that by the cdf based method.

Recently combination rules as well as the issue of conflict management in Dempster-Shafer theory have received considerable attention in information fusion research. Mostly these studies considered the combined mass assigned to the empty set as the conflict and have tried to provide alternatives to Dempster's rule of combination, which mainly differ in the way of how to manage the conflict. In this paper, we introduce a hybrid measure to judge the difference between two bodies of evidence as a basis for conflict analysis, and argue that using the combined mass assigned to the empty set as a whole to quantify conflict seems inappropriate. We then propose to use the discounting operator in association with the combination operator to resolve conflict when combining evidence, in which the discount rate of a basic probability assignment is defined using the entropy of its corresponding pignistic probability function. Finally, an application of this discounting and combination scheme to fusion of decisions in classifier combination is demonstrated.

This paper introduces a linguistic evaluation approach to new-product go/nogo evaluation at the front end in new-product development, based on the 2-tuple linguistic representation and the so-called preference-preserving 2-tuple transformation. A case study taken from the literature is used to illuminate the proposed technique and for a comparative study.

In many practical situations, users select between n alternatives a1,..., an, and the only information that we have about the utilities vi of these alternatives are bounds vi ≤ vi ≤ vi. In such situations, it is reasonable to assume that the values vi are independent and uniformly distributed on the corresponding intervals [vi, vi]. Under this assumption, we would like to estimate, for each i, the probability pi that the alternative ai will be selected. In this paper, we provide efficient algorithms for computing these probabilities.

This paper deals with the evaluation problem of Japanese traditional crafts in which product items are evaluated according so-called kansei features by means of the semantic differential method. As for traditional crafts, decisions on which items to buy or use are usually influenced by personal feelings/characteristics, we shall propose a consumer-oriented evaluation model targeting on those recommendation requests specified by consumers. Particularly, given a consumer’s recommendation request, the proposed model aims at defining an evaluation function that quantifies how well a product item meets the consumer’s feeling preference. An application for evaluating patterns of Kutani porcelain is conducted to illustrate how the proposed evaluation model works practically.

Keynote Addresses.- An Algebraic Approach to Substructural Logics - An Overview.- On Modeling of Uncertainty Measures and Observed Processes.- Statistics under Interval Uncertainty and Imprecise Probability.- Fast Algorithms for Computing Statistics under Interval Uncertainty: An Overview.- Trade-Off between Sample Size and Accuracy: Case of Static Measurements under Interval Uncertainty.- Trade-Off between Sample Size and Accuracy: Case of Dynamic Measurements under Interval Uncertainty.- Estimating Quality of Support Vector Machines Learning under Probabilistic and Interval Uncertainty: Algorithms and Computational Complexity.- Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion.- Uncertainty Modelling and Reasoning in Knowledge-Based Systems.- Label Semantics as a Framework for Granular Modelling.- Approximating Reasoning for Fuzzy-Based Information Retrieval.- Probabilistic Constraints for Inverse Problems.- The Evidential Reasoning Approach for Multi-attribute Decision Analysis under Both Fuzzy and Interval Uncertainty.- Modelling and Computing with Imprecise and Uncertain Properties in Object Bases.- Rough Sets and Belief Functions.- Several Reducts in Dominance-Based Rough Set Approach.- Topologies of Approximation Spaces of Rough Set Theory.- Uncertainty Reasoning in Rough Knowledge Discovery.- Semantics of the Relative Belief of Singletons.- A Lattice-Theoretic Interpretation of Independence of Frames.- Non-classical Logics.- Completions of Ordered Algebraic Structures: A Survey.- The Algebra of Truth Values of Type-2 Fuzzy Sets: A Survey.- Some Properties of Logic Functions over Multi-interval Truth Values.- Possible Semantics for a Common Framework of Probabilistic Logics.- A Unified Formulation of Deduction, Induction and Abduction Using Granularity Based on VPRS Models and Measure-Based Semantics for Modal Logics.- Information from Inconsistent Knowledge: A Probability Logic Approach.- Fuzziness and Uncertainty Analysis in Applications.- Personalized Recommendation for Traditional Crafts Using Fuzzy Correspondence Analysis with Kansei Data and OWA Operator.- A Probability-Based Approach to Consumer Oriented Evaluation of Traditional Craft Items Using Kansai Data.- Using Interval Function Approximation to Estimate Uncertainty.- Interval Forecasting of Crude Oil Price.- Automatic Classification for Decision Making of the Severeness of the Acute Radiation Syndrome.

The so-called measure of approximation quality plays an important role in many applications of rough set based data analysis. In this chapter, we provide an overview on various extensions of approximation quality based on rough-fuzzy and fuzzy-rough sets, along with highlighting their potential applications as well as future directions for research in the topic.

In this paper, we discuss a behavioral model of decision making using weather information, making use of the so-called fuzzy target based decision model. Due to forecasting uncertainty in weather forecasts, many decision problems in practice influenced by weather information have been formulated as that of decision making under uncertainty. After introducing the fuzzy target based decision model which states that, after assessing a fuzzy target, the decision maker should select the decision which maximizes the probability of meeting his target, we will show that different behaviors of the decision maker about his target can lead to different decisions. This behavioral analysis not only provides an interpretation for influence of psychological personality features of the decision maker on his decisions, but also has a corresponding link to attitudes towards risk in terms of utility function.

In practice, it is often necessary to make a decision under uncertainty. In the case of interval uncertainty, for each alternative i, instead of the exact value vi of the objective function, we only have an interval vi = [vi-,vi-.] of possible values. In this case, it is reasonable to assume that each value vi is uniformly distributed on the corresponding interval [vi-,vi-.], and to take the probability that Vi is the largest as the probability of selecting the i-th alternative. In some practical situations, we have fuzzy uncertainty, i.e., for every alternative i, we have a fuzzy number Vi describing the value of the objective function. Then, for every degree i, we have an interval Vi(alpha), the a-cut of the corresponding fuzzy number. For each alpha, we can assume the uniform distributions on the corresponding alpha-cuts and get a probability Pi(alpha) that vi will be selected for this alpha. From the practical viewpoint, it is desirable to combine these probabilities into a single probability corresponding to fuzzy uncertainty. In deriving the appropriate combination, we use the fact that fuzzy values are not uniquely defined, different procedures can lead to differently scaled values. It turns out that the only scaling-invariant distribution on the set of all the degrees alpha is a uniform distribution. So, we justify the choice of int Pi(alpha) dalpha as the probability that under fuzzy uncertainty, an alternative i will be selected.

Traditionally, decision-making problems that manage preferences from different experts follow a common resolution scheme composed of two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. In this paper, instead of using an aggregation operator to obtain a collective preference value, a random preference is defined for each alternative in the aggregation phase. To this end, a probability-based interpretation of weights is assumed. Then, the independent assumption imposed on alternatives allows us to work out probabilities of pairwise comparisons of random preferences. In order to obtain a raking order of alternatives for the exploitation phase, we define a choice function based on the ordered weight averaging (OWA) operator with help of the fuzzy majority.

This paper proposes a framework for weighted combination of classifiers for word sense disambiguation based on Dempster-Shafer theory of evidence. First, by taking the confidence of individual classifiers into account, weighted com- bination of individual classifiers corresponding to distinct repre- sentations of context of a polysemous word is formulated based on Dempster's rule of combination. Second, the developed model is improved by an adaptive strategy of weight determination and a ranking-and-combination scheme. The experiment conducted for four polysemous words, namely interest, line, serve ,a ndhard, shows significantly better results in comparison with previous studies on the same datasets.

It is widely accepted that a common precept for the choice under uncertainty is to use the expected utility maximization principle, which was established axiomatically. Recently, a formal equivalence between this principle of choice and the target-based principle, that suggests that one should select an action which maximizes the (expected) probability of meeting a (probabilistic) uncertain target, has been established and extensively discussed. In this paper, we discuss the issue of how to bring fuzzy targets within the reach of the target-based model for a class of decision making under uncertainty problems. Two methods for inducing utility functions from fuzzy targets are discussed and illustrated with an example taken from the literature.

In this paper, we aim at bringing fuzzy targets within the reach of the target-based decision model in decision analysis with uncertainty. After introduced the target-based interpretation of the expected value on which it is implicitly assumed a neutral behavior on attitude about target, we extend the target-based model for decision making under uncertainty using fuzzy targets, making use of Yager's procedure of converting possibility distributions into probability ones via a simple normalization. We also discuss a target-based framework of attitudinal decision making with uncertainty developed recently by Yager (1999).

In this paper we discuss a framework for weighted combination of classifiers in which each individual classifier uses a distinct representation of objects to be classified. This framework is essentially based on Dempster-Shafer theory of evidence (Dempster, 1967; Shafer, 1976) and OWA operators (Yager, 1988). It is of interest to see that this framework not only yields many commonly used decision rules without some strong assumptions made in the work by Kittler et al. (1998), but also provides other new decision rules. As an application, we apply the proposed framework of classifier combination to the problem of word sense disambiguation (shortly, WSD). To this end, we experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the discussed combination strategies are tested on the datasets for four polysemous words, namely interest, line, serve, and hard. The experiment conducted for these four polysemous words shows significantly better results in comparison with previous studies on the same datasets., The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html, IFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2120, Kobe, Japan, Symposium 5, Session 3 : Data/Text Mining from Large Databases Data Mining

It is well-known that linguistic decision-making problems that manage preferences from different experts follow a common resolution scheme composed by two phases: an aggregation phase that combines the individual preferences to obtain a collective preference value for each alternative; and an exploitation phase that orders the collective preferences according to a given criterion, to select the best alternative/s. In this paper we propose a probabilistic-based approach to multi-expert decision-making with linguistic information. To this end, instead of using an aggregation operator to obtain a collective preference, a random preference is defined for each alternative in the aggregation phase. Then, the so-called satisfaction principle suggests a linguistic choice function to establish a rank ordering among the alternatives. The method is illustrated by the same application example taken from the literature to compare with previous methods.

Arguing that various ways of using context in word sense disambiguation (WSD) can be considered as distinct representations of a polysemous word, a theoretical framework for the weighted combination of soft decisions generated by experts employing these distinct representations is proposed in this paper. Essentially, this approach is based on the Dempster-Shafer theory of evidence. By taking the confidence of individual classifiers into account, a general rule of weighted combination for classifiers is formulated, and then two particular combination schemes are derived. These proposed strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard.

This paper proposes a framework for combining classifiers based on OWA operators in which each individual classifier uses a distinct representation of objects to be classified. It is shown that this framework yields several commonly used decision rules but without some strong assumptions made in the work by Kittler et al. [7]. As an application, we apply the proposed framework of classifier combination to the problem of word sense disambiguation (shortly, WSD). To this end, we experimentally design a set of individual classifiers, each of which corresponds to a distinct representation type of context considered in the WSD literature, and then the proposed combination strategies are experimentally tested on the datasets for four polysemous words, namely interest, line, serve, and hard, and compared to previous studies.

In this paper, we first argue that various ways of using context in WSD can be considered as distinct representations of a polysemous word under consideration, then all these representations are used jointly to identify the meaning of the target word. Under such a consideration, we can then straightforwardly apply the general framework for combining classifiers developed in Kittler et al. [5] to WSD problem. This results in many commonly used decision rules for WSD. The experimental result shows that the multi-representation based combination strategy of classifiers outperform individual ones as well as known techniques of classifier combination in WSD.

In this paper, to deal with practical situations where a fuzzy classification must be approximated by available knowledge expressed in terms of a Pawlak's approximation space, we investigate an extension of approximation quality measure to a fuzzy classification aimed at providing a numerical characteristic for such situations. Furthermore, extensions of related coefficients such as the precision measure and the significance measure are also discussed. A simple example is given to illustrate the proposed notions.

Recently, the authors in [Huynh, V. N., Y. Namkamori (2005)] have introduced a formal notion of linguistic hierarchies in terms of ordered structure based semantics of the linguistic term sets for managing any hierarchical kind of multigranular sets of linguistic labels. In this paper, we develop a linguistic decision model based on the 2-tuples linguistic representation for MEDM in general multigranular linguistic contexts. This model allows us to associate a linguistic evaluation at the end of the decision process, instead of a real-valued choice function as developed in [Huynh, V. N., Y. Namkamori (2005)]. Consequently, this also allows us to consider different aggregation models and different selection processes for MEDM in general multigranular linguistic contexts.

It has been shown that, despite the differences in approach and interpretation, all belief function based models without the so-called dynamic component lead essentially to mathematically equivalent theories – at least in the finite case. In this paper, we first argue that at the logical level these models seem to share a common formal framework and various interpretations just come at the epistemic level. We then introduce a framework for belief modeling formally based on Dempster’s structure with adopting Smets’ view of the origin of beliefs. It is shown that the proposed model is more general than previous models, and may provide a suitable unified framework for belief modeling.

This paper aims to discuss how reasoning processes are represented in terms of granularization of possible worlds. Firstly, processes of classical inference are formulated from a point of view of reconstructing Kripke‐style models. The essential point of the reconstruction is that some possible worlds are amalgamated to generate granules of worlds and vice versa. Next, the idea is applied for fuzzy reasoning processes by considering fuzzy sets as fuzzily granularized possible worlds. One of advantages in the approach is that linguistic truth values with linguistic hedges are naturally introduced.

In this paper, we revisit the evidential reasoning (ER) approach to multiple-attribute decision making (MADM) with uncertainty. The attribute aggregation problem in MADM under uncertainty is generally formulated as a problem of evidence combination. Then several new aggregation schemes are proposed and simultaneously their theoretical features are explored. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the proposed techniques.

This paper introduces the notion of a fuzzy context model as a formal framework for representation and manipulation of vague knowledge. The motivation for the fuzzy context model arises from the consideration of several practical situations in data analysis, interpretation of vague concepts, and modeling expert knowledge for decision-making support. It is shown that the fuzzy context model can provide a constructive approach to fuzzy sets of type 2 emerged from a view-point of modeling vaguely conceptual knowledge as well as to a uncertainty measure of type 2, which is induced from vague knowledge expressed linguistically.

In this paper, we will briefly show that the notion of context model introduced by Gebhardt and Kruse (1993) can be considered as a unifying framework for representing fuzziness and uncertainty. Firstly, from a decision making point of view, the Dempster- Shafer theory of evidence will be reinterpreted within the framework of context model. Secondly, from a concept analysis point of view, the context model will be semantically considered as a data model for constructing membership functions of fuzzy concepts in connection with likelihood as well as random set views on the interpretation of membership grades. Furthermore, an interpretation of mass assignments of fuzzy concepts within the context model is also established.

In this paper we show that the context model proposed by Gebhardt and Kruse (1993) can be semantically extended and considered as a data model for constructing membership functions of fuzzy concepts within the framework of meta-theory developed by Resconi et al. in 1990s. Within this framework, we integrate context models by using a model of modal logic, and develop a method for calculating the expressions for the membership functions of composed fuzzy concepts based on values {0, 1}, which correspond to the truth values {F, T} assigned to a given sentence as the response of a context considered as a possible world. It is of interest that fuzzy intersection and fuzzy union operators by this model are truth-functional, and, moreover, they form a well-known dual pair of Product t-norm TP and Probabilistic Sum t-conorm SP.

Editorial for the special issue on Interval/Probabilistic Uncertainty.

In this paper, a framework for representing vague knowledge based on the notion of context model introduced by Gebhardt and Kruse (1993) is discussed. From a concept analysis point of view, it has been shown that the context model can be semantically considered as a data model for fuzzy concept analysis (Huynh et al., 2004). From a decision analysis point of view, in order to deal with the problem of synthesis of vague evidence linguistically provided by experts in some situations of decision analysis, the notions of context-dependent vague characteristics and fuzzy context model will be introduced. It is shown that each context-dependent vague characteristic within fuzzy context model directly induces a uncertainty measure of type 2 interpreted as “vague” belief function, which is inferred from vague evidence expressed linguistically.