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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]

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]

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]

The most important problem in the design of control systems by the LQ regulator theory is the absence of guidelines to determine the weighting coefficient matrices Q and R in the quadratic objective function. In practice, a designer determines Q and R by trial-and-error while observing the step response waveform. A designer modifies Q and R and observes the obtained response waveforms. While making a compromise with design specifications for a considered system, a designer tacitly sets the tolerance for response waveform in the sense that he can tolerate the deviation from the design specification. In contrast to the traditional method of design, this paper formulates the tolerance for a response waveform conceived by a designer for design specifications as a fuzzy constraint using fuzzy set theory. A method of design is proposed in which the fuzzy representation for the weighting coefficient matrices is constructed for given design specifications, and the satisfactory solution (Q*, R*) to satisfy the designer's standard is determined. An appreciation for the aircraft is shown. [ABSTRACT FROM AUTHOR]

This paper develops a mathematical model for intermodal freight transportation. It focuses on determining the flow of goods, the number of vehicles, and the transferred volume of goods transported from origin points to destination points. The model of this article is to minimize the total cost, which consists of fixed costs, transportation costs, intermodal transfer costs, and CO2 emission costs. It presents a mixed integer linear programming (MILP) model that minimizes total costs, and a fuzzy mixed integer linear programming (FMILP) model that minimizes imprecise total costs under conditions of uncertain data. In the models, node capacity, detour, and vehicle utilization are incorporated to estimate the performance impact. Additionally, a computational experiment is carried out to evaluate the impact of each constraint and to analyze the characteristics of the models under different scenarios. Developed models are tested using real data from a case study in Southern Vietnam in order to demonstrate their effectiveness. The results indicate that, although the objective function (total cost) increased by 20%, the problem became more realistic to address when the model was utilized to solve the constraints of node capacity, detour, and vehicle utilization. In addition, on the basis of the FMILP model, fuzziness is considered in order to investigate the impact of uncertainty in important model parameters. The optimal robust solution shows that the total cost of the FMILP model is enhanced by 4% compared with the total cost of the deterministic model. Another key measurement related to the achievement of global sustainable development goals is considered, reducing the additional intermodal transfer cost and the cost of CO2 emissions in the objective function. [ABSTRACT FROM AUTHOR]

Establishing a service level agreement (SLA) between a cloud provider and a cloud consumer is becoming increasingly critical: consumers expect a specified quality of service (QoS) for their cloud applications, and providers must be able to guarantee that the agreed-upon QoS will be maintained. These concepts require the SLA negotiation to be performed in a manner whereby a provider and a consumer can effectively bargain on various QoS preferences, such as price, response time and service level. This paper presents a novel agent-based fuzzy constraint-directed negotiation (AFCN) model for SLA negotiation. It provides a framework for integrating time, resource (market) and behavioral factors into the decision making process for service level agreements and cloud load balancing. The proposed AFCN model supports an iterative many-to-many bargaining negotiation infrastructure that is a fully distributed and autonomous approach and that does not require a broker to coordinate the negotiation process. The novelty of the proposed model is that it uses the concept of a fuzzy membership function to represent imprecise QoS preferences. This added information sharing is critical for the effectiveness of distributed coordination. It can not only speed up the convergence but also enforce global consistency through iterative exchanges of offers and counter-offers with limited information sharing and without privacy breaches. To consider the behavior of different agents, the AFCN model can also flexibly adopt different negotiation strategies such as the competitive, win-win, and collaborative strategies in different cloud computing environments. The experimental results demonstrate that the proposed model consistently outperforms other agent-based SLA negotiation models in terms of the degree of satisfaction, the ratio of successful negotiation, the buying price of the consumer agent (CA), the revenue of the provider agent (PA), and the convergence speed. Consequently, the proposed AFCN is both flexible and useful for fully distributed SLA negotiations. [ABSTRACT FROM AUTHOR]