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Background: Agent negotiation is widely used in e-commerce negotiation, cloud service service-level agreements, and power transactions. However, few studies have adapted alternative negotiation models to negotiation processes between healthcare professionals and patients due to the fuzziness, ethics, and importance of medical decision making. Method: We propose a Bayesian learning based bilateral fuzzy constraint agent negotiation model (BLFCAN). It support mutually beneficial agreement on treatment between doctors and patients. The proposed model expresses the imprecise preferences and behaviors of doctors and patients through fuzzy constrained agents. To improve negotiation efficiency and social welfare, the Bayesian learning method is adopted in the proposed model to predict the opponent's preference. Results: The proposed model achieves 55.4% to 64.2% satisfaction for doctors and 69-74.5% satisfaction for patients in terms of individual satisfaction. In addition, the proposed BLFCAN can increase overall satisfaction by 26.5-29% in fewer rounds, and it can alter the negotiation strategy in a flexible manner for various negotiation scenarios. Conclusions: BLFCAN reduces communication time and cost, helps avoid potential conflicts, and reduces the impact of emotions and biases on decision-making. In addition, the BLFCAN model improves the agreement satisfaction of both parties and the total social welfare. [ABSTRACT FROM AUTHOR]

In this paper, the modified SMF system is used in the real MPS problem. This problem occurs in the production planning process where the decision maker plays an important role in making decisions in an uncertain environment. As researchers, we are trying to find the best solution for the final decision maker. SMF analyzed FLP production equipment using data actually collected from chocolate production companies. The problem of MPS incompatibility has been described. The aim of this article is to find the best UOP with high satisfaction and nonsense as the main thing. Since there are so many decisions to make, the best UOP table is defined in terms of insensitivity and satisfaction to find a solution with a high UOP level and a high level of satisfaction. OF indicates that a high UOP will not lead to a high level of satisfaction. The results of this work suggest that the best decision is based on the negative impact on the FS of the MPS. In addition, a high level of UOP is achieved when the blur is low. [ABSTRACT FROM AUTHOR]

Abstract Background The clinical practice of shared decision-making (SDM) has grown in importance. However, most studies on SDM practice concentrated on providing auxiliary knowledge from the third-party standpoint without consideration for the value preferences of doctors and patients. The essences of these methods are complete and manual negotiation, and the problems of high cost, time consumption, delayed response, and decision fatigue are serious. Methods In response to the above limitations, this article proposes a fuzzy constraint-directed agent-based negotiation and recommendation framework for bilateral and multi-issue preference negotiation in SDM (PN-SDM). Its purpose is to provide preference information and intellectualize PN-SDM to promote SDM practice. We modeled PN-SDM problems as distributed fuzzy constraint satisfaction problems and designed the doctor agent and patient agent to negotiate on behalf of the doctor and patient. The negotiation result was then transformed into treatment plans by the recommendation model. The proposed negotiation and recommendation models were introduced in detail by an instance. Results The proposed method with different strategies and negotiation pairs achieves good performance in terms of negotiation running time, negotiation rounds, and combined aggregated satisfaction value. Specifically, it can feasibly and effectively complete multiple rounds of PN-SDM in a few seconds and obtain higher satisfaction. Conclusion The experimental results indicate that the negotiation model can effectively simulate preference negotiation and relieve the pressure of increasing issues. The recommendation model can assist in decision-making and help to realize SDM. In addition, it can flexibly cope with various negotiation scenarios by using different negotiation strategies (e.g., collaborative, win–win, and competitive).

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power system. Mobile energy storage systems (MESSs) have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ability. However, when urban traffic flows rise, the congested traffic environment will prolong the transit time of MESS, which will ultimately affect the operation state of the power networks and the economic benefits of MESS. This paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks, considering road congestion and the operation constraints of the power networks. The upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator (DNO) in order to maximize the total revenue of the system. With fuzzy time windows and fuzzy road congestion indexes, the lower-level model optimizes the route for the transit problem of MESS. Therefore, road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation scheme. Both the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed model. The simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road congestion. Besides, the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

Background: The clinical practice of shared decision-making (SDM) has grown in importance. However, most studies on SDM practice concentrated on providing auxiliary knowledge from the third-party standpoint without consideration for the value preferences of doctors and patients. The essences of these methods are complete and manual negotiation, and the problems of high cost, time consumption, delayed response, and decision fatigue are serious.Methods: In response to the above limitations, this article proposes a fuzzy constraint-directed agent-based negotiation and recommendation framework for bilateral and multi-issue preference negotiation in SDM (PN-SDM). Its purpose is to provide preference information and intellectualize PN-SDM to promote SDM practice. We modeled PN-SDM problems as distributed fuzzy constraint satisfaction problems and designed the doctor agent and patient agent to negotiate on behalf of the doctor and patient. The negotiation result was then transformed into treatment plans by the recommendation model. The proposed negotiation and recommendation models were introduced in detail by an instance.Results: The proposed method with different strategies and negotiation pairs achieves good performance in terms of negotiation running time, negotiation rounds, and combined aggregated satisfaction value. Specifically, it can feasibly and effectively complete multiple rounds of PN-SDM in a few seconds and obtain higher satisfaction.Conclusion: The experimental results indicate that the negotiation model can effectively simulate preference negotiation and relieve the pressure of increasing issues. The recommendation model can assist in decision-making and help to realize SDM. In addition, it can flexibly cope with various negotiation scenarios by using different negotiation strategies (e.g., collaborative, win-win, and competitive). [ABSTRACT FROM AUTHOR]

The task of outlier mining is of particular interest in detecting the abnormal data instances in high dimensional datasets. However, the existing outlier detection techniques are not able to produce efficient results for high dimensional datasets. To improve the efficiency of mining, a novel outlier detection method is proposed that aims to reduce the dimensions of the dataset, by integrating fuzzy constraint with feature correlation, thereby making the dataset a sparse search space. The feature correlation analysis uses the sparse factor threshold to identify the irrelevant features and eliminates them by determining the correlation factor, whereas the fuzzy constraint uses nearness measure to identify the similarities between data instances and the constraint information. Finally, the outlier set of the reduced dataset is obtained by searching the sparse subspaces and implementing the Particle Swarm Optimization. When searching the sparse subspace, the sparse degree threshold is introduced to represent the degree of sparsity of the data. The significance of the proposed method lies in the integration of feature correlation with fuzzy constraint providing two-step data reduction. This approach not only decreases the execution time but also improves the accuracy. The experimental results are carried out on synthetic and benchmark datasets and the outcome reveals that the proposed mining outperforms the existing techniques and improves the efficiency and accuracy in detecting local outliers. [ABSTRACT FROM AUTHOR]

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.

Quadratic Programming has been widely applied to solve real-world problems. This paper describes a solution method for solving a special class of fuzzy quadratic programming problems with fuzziness in relations. Then the method is generalized to a more general fuzzy quadratic programming problem, where the cost coefficients, the matrix of the quadratic form, constraints coefficients, and the right-hand sides are all fuzzy numbers. Finally, some examples are taken to the utility of our proposed method.

Fuzzy multi-objective zero-one linear programming (FMOZOLP) has many applications in various fields such as assembly line balancing, assignment, project portfolio selection and maximal covering location problems. In many of the existing methods for solving FMOZOLP problems, membership degree of different points of a fuzzy number is not considered or by performing α-cut, points with membership function more than or equal to α, are included in calculations. However, even in this case, membership degree of these points has no effect on optimal solution. In this paper, in addition to modifying defects and failures of Yu and Li method [1] in solving fuzzy zero-one linear programming problems, we develop a novel approach to solve FMOZOLP problems considering membership degree of coefficients. Finally, an illustrative example for the project portfolio selection is included to compare results obtained by the proposed approach with results obtained by the other fuzzy methods.

Based on the improved particle swarm optimization (PSO) algorithm, an optimization approach for the cargo oil tank design (COTD) is presented in this paper. The purpose is to design an optimal overall dimension of the cargo oil tank (COT) under various kinds of constraints in the preliminary design stage. A non-linear programming model is built to simulate the optimization design, in which the requirements and rules for COTD are used as the constraints. Considering the distance between the inner shell and hull, a fuzzy constraint is used to express the feasibility degree of the double-hull configuration. In terms of the characteristic of COTD, the PSO algorithm is improved to solve this problem. A bivariate extremum strategy is presented to deal with the fuzzy constraint, by which the maximum and minimum cargo capacities are obtained simultaneously. Finally, the simulation demonstrates the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Natural language question-and-answering is one of the most convenient means for communicating with the Semantic Web, which is typically in the form of online knowledge bases encoded in Web Ontology Language (OWL). To understand a natural language question, it is essential that it can be translated into a query that is understandable by the knowledge bases. This article is concerned with the task of semantic mapping from natural language questions to OWL queries, and proposes an automatic and domain-independent mapping framework, called Three-Phases Semantic Mapping (TPSM). The TPSM framework approaches the task of semantic mapping in three interrelated phases: (i) formalizing knowledge, (ii) building semantic mapping, and (iii) combining OWL queries. First, formalizing knowledge formalizes the units of mapping in the natural language and OWL knowledge. Second, semantic mapping builds the transverse mapping between questions and OWL knowledge, as formalized in the first phase, by means of working with the so-called Fuzzy Constraint Satisfaction Problems (FCSP). Third, combining OWL queries obtains valid Resource Description Framework (RDF) models by applying predefined templates and their corresponding combining methods. We have implemented a prototype semantic mapping system based on the framework, and have conducted a series of experimental validations involving OWL knowledge bases in different domains as queried by various types of questions. [ABSTRACT FROM AUTHOR]

This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their evaluation concepts and agents facilitate student-student negotiations during the assessment process. By applying the proposed method, agents can achieve mutually acceptable agreements that avoid the subjective judgments and unfair assessments. Thus, the negotiated agreement provides students with superior assessments, thereby enhancing learning effectiveness. To demonstrate the usefulness of the proposed framework, a web-based assessment agent was implemented and used by 49 information management students who submitted assignments for peer review. Experimental results suggested that students using the system had significantly improved learning performance over three rounds of peer assessment. Questionnaire results indicated that students believed that the assessment agent provided increased flexibility and equity during the peer assessment process. [ABSTRACT FROM AUTHOR]

Abstract: Let I be a finite or infinite index set, X be a topological space and be a family of finitely continuous topological spaces (in short, FC-space). For each , let be a set-valued mapping. Some existence theorems of maximal elements for the family are established under noncompact setting of FC-spaces. As applications, some equilibrium existence theorems for generalized games with fuzzy constraint correspondences are proved in noncompact FC-spaces. These theorems improve, unify and generalize many important results in recent literature. [Copyright &y& Elsevier]

A method of a priori information consideration, based on description of definition domain of regression parameters by equality constraints and by inequality constraints, where either right-hand or left-hand parts of constraints are prescribed fuzzy, is suggested. Statistical properties of estimates are studied. The described technique is applied for derivation of models of non-stationary stochastic systems, which can be reduced to a model of regression with switchings. [ABSTRACT FROM AUTHOR]