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Outlier mining task is to discover some unusual objects, and however, most existing methods and their mining results lack pertinence. To address the pertinence of outlier results, we propose a novel outlier detection approach, namely, FOD, which aims at finding anomalies in full dimensions that lack pertinence and understandability. Our key idea is to use fuzzy constraint technology to prune irrelevant objects for outlier detection, during which the nearness measure theory in fuzzy mathematics is used for detecting similarities between objects and constraint information. FOD finds outlier by searching sparse subspace, where genetic algorithms can be extended and incorporated into FOD such that an optimum solution of an anomaly is discovered. While constructing a sparse subspace, we present the sparse threshold concept to describe the sparse levels of data objects in a subspace, where data objects are regarded as anomalies. Then, we demonstrate the effectiveness and scalability of our method on synthetic and UCI datasets. The experiment evaluations reveal that our fuzzy constraint-based outlier detection is superior to two existing full dimensional algorithms. Moreover, FOD algorithm also improves the accuracy of outlier detection.

In order to determine layout location of variable message signs (VMS) scientifically and reasonably and give full play to its transportation guidance effect, the optimal selection method of expressways VMS layout location is studied based on general rules and requirements of expressways network VMS layout. Construct the multi-objective optimization model of VMS information utilization maximum, VMS effectiveness and VMS total cost minimum based on fuzzy constraint conditions, and the Hang-Jin-Qu expressway VMS layout is analyzed. The results show that the proposed multi-objective optimization model is more in line with the actual need of VMS layout and easy to operate, and has important practical value.

In order to improve learning efficiency and quantify the quality of teaching, a method of modelling the influence of emotional factors on students' learning efficiency is proposed. The statistical feature analysis object model is constructed. The quantitative regression analysis method is used to construct the big data model of emotional factors and learning efficiency. The association rule decomposition method is used to decompose and model the big data samples, and analyse the influence factors of emotion on learning efficiency. The fuzzy constraint control method clusters and mines big data association rules, and analyses the promotion factors of emotional factors to students' learning efficiency, calculates the level of promotion contribution, and adopts adaptive evolutionary game method to realise emotional factors under big data. Modelling the impact of student learning efficiency, experiments show that the proposed method has high confidence level and small error in analysing subject problems.

The crop planning problem is often formulated as a linear programming problem. But, in many actual cases, the profit coefficients for agricultural products are not certain values because of the influence of the future weather, so a linear programming model with constant coefficients does not describe the environment of decision making properly. Therefore, we propose a model of crop planning with uncertain (stochastic) values which may support decision making of agricultural farms. In this paper, we treat such uncertain elements as the values with the fuzziness and randomness.

The main purpose of hypothesis assessment is to define the order of hypothesis evaluation inside an automated diagnostic process. If a reasonable ordering of symptoms can be established, the hypothesis evaluation step can start with checking the most plausible faults, so fault causes may be found even by the first evaluation tests. This way hypothesis assessment can determine the efficiency of the whole diagnostic process. Our hypothesis assessment method is based upon structural and behavioral system models implemented in the form of fuzzy constraint networks. The article also presents details about a case study application for evaluating the approach, where a sugar evaporation technology has been modeled.

In a multi-state system, both the system and its components are allowed to experience more than two possible states, e.g. completely working, partially working or partially failed, and completely failed. In this paper reliability evaluation and reliability optimization of k-out-of-n systems are studied for the case where the components and the system have multiple performance levels. A recursive algorithm is used for reliability evaluation of multi-state k-out-of-n systems with independent components. Then a fuzzy model is presented for minimization total cost subject to reliability constraint. In the fuzzy model a fuzzy constraint is used for reliability that may we use different weights to constraint and objective function, and as a consequence the amount of importance of each objective function and reliability constraint can consider. Finally a numerical example is given.

We present an approach for integrating explicit knowledge about the spatial context of objects into image region labelling. Our approach is based on spatial prototypes that represent the typical arrangement of objects in images. We use Fuzzy Constraint Satisfaction Problems as the underlying formal model for producing a labelling that is consistent with the spatial constraints of prototypes.

In this paper we present an experimental analysis of a fuzzy constraint based framework for automated purchase negotiations in competitive trading environments. The main goal of this work is to show by experimental analysis which combination of different agents' negotiation attitudes allows buyer and seller agents to improve the multi-attribute bilateral negotiation processes. Agents' attitudes are related to the agents' strategic behaviour in the negotiation process, where strategic behaviours are described in terms of expressiveness and receptiveness.

This paper presents a new fuzzy constraint based multi-issue negotiation model for agent mediated collaborative manufacturing. Unlike the related work in fuzzy constraint based negotiation models, the pressure of deadline is taken into account while designing agents' demand relaxation strategies. We give an analysis of the optimal concession strategies and the analysis shows the existence of equilibrium even when all the trading parties have incomplete information about one another. Moreover, outside options are specifically considered while analyzing agents' optimal strategies and equilibrium solutions.

This paper presents a fuzzy constraint based model for bilateral multi-attribute agent purchase negotiations in competitive trading environments. Argumentation is used as a key mechanism to improve agreements, in contrast to other fuzzy constraint based models which are limited to quantitative offers and counter-offers. A set of locutions and decision mechanisms which fire them are specified, so that each agent may decide its degree of cooperation and expressiveness.

Answer validation is an important component of any question answering system. In this paper we show how the formalism of prioritized fuzzy constraint satisfaction allows to unify and generalize some common validation strategies. Moreover, answer candidates are represented by fuzzy sets, which allows to handle imprecise answers.

Diet problems are usually modelled as linear programming problems and then solved by any adequate method. Although this seems a valid approach, is almost impossible to have a perfect knowledge of all the data taking part into a particular diet problem. Such uncertainty must be managed in some way and here is where the theory of fuzzy sets and systems can show all its power. We address a problem arising in Argentinean farms where the cattle is not confined. As a consequence, it is impossible to assure the amount of food each animal will eat and then, it has no sense to design diets with exact requirements. In order to solve the problem, we formulate it as a fuzzy diet problem and provide a computational tool to help cattlemen in the diet design process.

In this work, we present a Disjunctive Fuzzy Constraint Networks model for continuous domains, which generalizes the Disjunctive Fuzzy Temporal Constraint Networks model for temporal reasoning, and we propose the use of the series-parallel and tree-decomposition approaches for simplifying its processing. After a separate empirical evaluation process of both techniques, a combined evaluation process over the same problem repository has been carried out, finding that series-parallel problems practically subsume tree-decomposable problems.

The paper advocates the interest of distinguishing between negative and positive preferences in the processing of flexible queries. Negative preferences express what is (more or less, or completely) impossible or undesirable, and by complementation state flexible constraints restricting the possible or acceptable values. Positive preferences are not compulsory, but rather express wishes; they state what attribute values would be really satisfactory. The paper discusses the handling of bipolar queries, i.e. queries involving negative and positive preferences, in the framework of possibility theory. Both ordinary queries expressed in terms of requirements, and case-based queries referring to examples, are considered in this perspective.

Now-a-days, companies have to face individual customer demands and rapid technological changes. Beside the fulfilment of quality and cost requirements companies are forced to realise a shortened time-to-market to maintain and increase competitiveness. One approach to meet these requirements is Simultaneous Engineering, the integrated and time parallel design of products and processes.

In the product development process, customer requirements as well as constraints concerning the entire product life cycle have to be satisfied. Introducing the concept of Concurrent Engineering (CE), interdependencies of constraint sets increase due to the parallelization of activities. Constraint systems are used to attack this problem. Fuzzy Constraint Networks (FCN) have been introduced to expand the employment of constraint systems to the early phases of the product development process.