Linguistic-labels aggregation and consensus measure for autocratic decision making using group recommendations

Group decision making is a common and important activity in everyday life. In many cases, due to inherent uncertainty, experts cannot express their score or preference using exact numbers. The use of linguistic labels makes expert judgment more reliable and informative for decision-making. One of the problems of group decision making in fuzzy domains is aggregating experts' opinions, expressed using linguistic labels, into a group opinion. This aggregation allows the group to select the most "preferred" alternative from a finite set of candidates. The aggregation of individual judgments into a group opinion requires a measured level of consensus. In this paper, by introducing a new linguistic-labels aggregation operation, we present a procedure for handling an autocratic group decision-making process under linguistic assessments. The methodology presented results in two consequent outcomes: a group-based recommendation, and a score for each expert, reflecting the expert's contribution towards the group recommendation. By changing the weights of the experts based on their contributions, we increase the consensus and reinforce the common decision, without forcing the experts to modify their opinions. This methodology allows an autocratic decision maker to use a diversified group of consultants for a succession of decisions reaching a high level of consensus.     

Model-driven trust negotiation for Web services

Trust negotiation is an approach to access control whereby access is granted based on trust established in a negotiation between the service requester and the service provider. Trust negotiation systems avoid several problems facing traditional access control models such as DAC (discretionary access control) and MAC (mandatory access control). Another problem is that Web service providers often do not know requesters identities in advance because of the ubiquitousness of services. We describe Trust-Serv, a trust negotiation framework for Web services, which features a policy language based on state machines. It is supported by lifecycle management and automated runtime enforcement tools. Credential retrieval and validation in Trust-Serv rely on predefined Web services that provide interactions with attribute assertion authorities and public key infrastructure.     

On decentralized negotiation of optimal consensus

A consensus problem consists of finding a distributed control strategy that brings the state or output of a group of agents to a common value, a consensus point. In this paper, we propose a negotiation algorithm that computes an optimal consensus point for agents modeled as linear control systems subject to convex input constraints and linear state constraints. By primal decomposition and incremental subgradient methods, it is shown that the algorithm can be implemented such that each agent exchanges only a small amount of information per iteration with its neighbors.     

面向云联网的云服务协商机制

随着云计算的快速发展,越来越多的用户开始使用云服务提供商提供的服务,而云联网作为云计算研究的新领域,可以实现跨云服务提供商的服务,当单个云服务提供商无法满足用户的服务需求时,云服务提供商之间以合作的方式为用户提供服务,以便更好地满足用户的服务需求。针对上述情况,提出了面向云联网的云服务协商机制,该机制利用云联网和改进的经典合同网模型来实现云服务提供商的交互协商。为了有效地选出合作伙伴以提高合作效率,还为每一个云服务提供商建立了一个熟人集。实验表明,本文设计的机制可以有效地提高云服务提供商之间的合作效率,并且可以更好地满足用户的服务需求。     

A similarity measure for the negotiation in web services

This paper presents a new negotiation approach integrating a similarity-based metric to measure the client’s satisfaction level by quantifying the correspondence between the features required by the client and the ones proposed by the service. This negotiation process is integrated within an adaptation platform for multimedia presentations supporting different types of terminals. The negotiation and adaptation processes are based on the management of user’s and service’s profiles. We also propose an extension of the SIP protocol to ensure the communication between the client and the server by defining new functionalities.     

Multi-group QoS consensus for web services

QoS has been considered as a significant factor for web service marketing and selection. The interpretation of QoS value from web service consumers and providers would be very different. However, a large group of web service participants with different backgrounds may have difficulties in reaching consensus on the values of multi-dimensional web service QoS, so they may have to be clustered in multi-groups in order to improve effectiveness and efficiency. The similarity of clustered fuzzy QoS dispositions as well as their preference order over these attributes should be analyzed to form a multi-groups consensus framework. A soft multi-groups clustering approach could be adopted to prevent opinions from being excluded unintentionally. The group boundaries and similarity thresholds which are used for clustering and analyzing fuzzy QoS opinions can be moderated dynamically according to the feedback from the internal learning mechanism and the web service consumers. As a result, a model for marketing web services based on multi-group consumers' QoS consensus, the FMG-QCMA (Fuzzy Multi-Groups based QoS Consensus Moderation Approach), is proposed to meet the above requirements. The proposed FMG-QCMA is also evaluated through a case study to demonstrate its effectiveness and efficiency in relation to an existing framework, QCMA (QoS Consensus Moderation Approach).     

A consensus mechanism based on multi-round concession negotiation

Consensus is the rule that maintains the normal operation of the blockchain. The formation of consensus is a process that allows each node in the blockchain to maintain consistent data. However, the existing consensus formation methods lack concession negotiation. The process of consensus formation is too rigid, which leads to the consensus reached rate is not high, and the satisfaction of nodes across the network decreases. In this paper, we propose a proposition consensus mechanism based on multiple rounds of concession negotiation. This mechanism quantifies the node’s satisfaction with the proposition by weighted voting at first, and then designs the proposition consensus algorithm to describe the concession negotiation process of blockchain nodes. Finally, through experiments and theoretical analysis, the effectiveness of the mechanism proposed in this paper is verified, which significantly improves the consensus reached rate and the overall satisfaction of nodes across the network.     

A trust negotiation system for digital library Web services

A scalable approach to trust negotiation is required in digital library (DL) environments that have large and dynamic user populations. In this paper we introduce Trust-Serv, a model-driven trust negotiation framework for Web services, and show how it can be used to effectively handle trust negotiation in DLs. The framework employs a model for trust negotiation based on state machines, extended with security abstractions. High-level specifications expressed with the state-machine-based model are then translated into formats suitable for automating the trust negotiation process. The proposed framework also supports negotiation policy lifecycle management, an important trait in the dynamic environments that characterize DLs. In particular, we present a set of policy change operations that enable the dynamic evolution of negotiation policies without disrupting ongoing negotiations. The proposed approach has been implemented as a container-centric mechanism that is transparent to the DL and to the developers of DL Web services, simplifying DL development and management as well as enabling scalable deployments.     

Intelligent negotiation model for ubiquitous group decision scenarios

Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents’ modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents’ reasoning, and intelligent dialogues.     

A three-phase process for SLA negotiation of composite cloud services

Negotiation is used when two or more parties with conflicting goals pursue their own interests. Service trading in cloud computing is a case in which automated negotiation system (ANS) can be used to achieve high satisfaction levels for both contract parties. An ANS allows the parties negotiate automatically on some parameters that are important for them. Multiple services are combined together to provide a composite service that delivers a value-added service to consumers. With the increasing tendency to use composite cloud services, there is a need for designing ANSs capable of fulfilling requirements of consumers of these services. Unlike existing ANSs which support negotiation of single service, our proposed ANS supports negotiation of composite service. This work also proposes a novel negotiation strategy to enhance satisfaction of both trading parties. Some simulations were carried out to evaluate the performance of proposed negotiation strategy in terms of negotiating agents’ utility and negotiation speed. Analysis of the results of simulations shows that our recommended strategy provides more performance than the others.     

Evolutionary learning approach to multi-agent negotiation for group recommender systems

Recommender systems (RSs) have emerged as a solution to the information overload problem by filtering and presenting the users with information, services etc. according to their preferences. RSs research has focused on algorithms for recommending items for individual users. However, in certain domains, it may be desirable to be able to recommend items for a group of persons, e.g., movies, restaurants, etc. for which some remarkable group recommender systems (GRSs) have been developed. GRSs provide recommendations to groups, i.e., they take all individual group members’ preferences into account and satisfy them optimally with a sequence of items. Taking into consideration the fact that each group member has different behaviour with respect to other members in the group, we propose a genetic algorithm (GA) based multi-agent negotiation scheme for GRS (GA-MANS-GRS) where each agent acts on behalf of one group member. The GA-MANS-GRS is modelled as many one-to-one bilateral negotiation schemes with two phases. In the negotiation phase, we have applied GA to obtain the maximum utility offer for each user and generated the most appropriate ranking for each individual in the group. For the recommendation generation phase, again GA is employed to produce the list of ratings with that minimizes the sum of distances among the preferences of the group members. Finally, the results of computational experiments are presented that establish the superiority of our proposed model over baseline GRSs techniques.

     

Generating rules from data mining for collaboration moderator services

A Moderator is a knowledge based system that supports collaborative working by raising awareness of the priorities and requirements of other team members. However, the amount of advice a Moderator can provide is limited by the knowledge it contains on team members. The use of data mining techniques can contribute towards automating the process of knowledge acquisition for a Moderator and enable hidden data patterns and relationships to be discovered to facilitate the moderation process. A novel approach is presented, consisting of a knowledge discovery framework which provides a semi-automatic methodology to generate rules by inserting relationships discovered as a result of data mining into a generic template. To demonstrate the knowledge discovery framework methodology an application case is described. The application case acquires knowledge for a Moderator to make project partners aware of how to best formulate a proposal for a European research project by data mining summaries of successful past projects. Findings from the application case are presented.     

Web personalization for user acceptance of technology: An empirical investigation of E-government services

E-Commerce firms have adopted Web Personalization techniques extensively in the form of recommender systems for influencing user behavior for customer retention. Although there are numerous studies in this area, academic research addressing the role of Web Personalization in user acceptance of technology is very scant. Further, owing to the potential of recommender systems to attract and retain customers, most studies in web personalization have been done in E-Commerce setting. In this research, the ‘Consumer Acceptance and Use of Information Technology’ theory proposed in previous research has been extended to include web personalization as a moderator and has been tested in an E-Government context. Data collection involved conducting a laboratory experiment with the treatment group receiving personalized web forms for requesting an E-Government service. Our analyses show that personalizing the Web by self-reference and content relevance has a significant moderator role in influencing the relationship between determinants of intention to use and behavioral intention in certain cases.     

Context-aware enhancement of personalization services: A method of power optimization

Context-aware applications, which consist of a sensor system, a reasoning system and service artifacts such as mobile devices, kiosks and robots, require data from the sensors to be queried on a continuous basis. The smaller the sensing interval and the greater the amount of service time, the more accurate the service, but the more energy is consumed. Thus, use of context-aware applications always involves a trade-off. In this paper, we propose an automatic method of optimizing the level of personalization involving the sensing cycle and service time of a personalized application. The method proposes a quadratic form of total cost curve which demonstrated that the minimum identified value is always the global optimum. This eliminates the necessity of an exhaustive search for the minimum value for all levels of personalization.     

基于纳什谈判的共享经济区块链网络PoS共识传播博弈分析

针对共享经济中的信任问题,利用区块链技术搭建基于PoS(权益证明)共识机制的共享经济区块链网络,实现去信任化和去中心化.在Po S共识机制中,区块链用户提供交易费用,从而激励矿池招募验证者进行区块传播验证,区块链用户与矿池都能从更多的验证者数量中获益,但验证者越多,双方付出的成本越高.首先,从纳什谈判的角度研究共享经济区块链网络PoS共识机制中区块链用户与矿池的利益冲突问题,在交易费用与验证者数量之间进行权衡,构建纳什谈判博弈模型;然后,证明区块链用户与矿池的纳什谈判博弈模型中纳什谈判解的存在性和唯一性,得到区块链用户和矿池的最优决策;最后,通过数值算例分析通信成本和验证者数量对区块链用户和矿池的最优决策以及效用的影响.研究结果表明:区块链用户与矿池都能从较低的通信成本中获益;纳什谈判博弈模型能够有效解决共享经济区块链网络中用户与矿池的利益冲突,提高网络效率.     

Product architecting for personalization

Personalization is an emerging manufacturing paradigm whereby customers can tailor products to their individual needs while maintaining high production efficiency. This paradigm necessitates “personalized product architecting” for determination of customizable/personalizable product modules and cost-effective manufacturing methods. This paper presents an initial effort in developing a method for identifying appropriate product architectures and manufacturing resolutions to achieve personalization considering functional utility and manufacturing cost. Ergonomic experiments and conjoint analysis are implemented to build functions relating manufacturability, price, and utility. Using these functions, a case study based on shoe products is conducted and the common integer programming welfare problem is expanded to a mixed-integer programming optimization problem for determination of a product family incorporating both personalized and customized offerings.     

Design and implementation of a smart system for personalization and accurate selection of mobile services

Wireless mobile services are computing applications that run on handheld wireless devices. Such applications must work within the daunting constraints of the devices, which include memory, processing power, input capabilities, and size of display. It is therefore important that mobile services take into account the user’s context, optimize resource usage, and minimize input effort imposed on the user. In this paper, we present the design and implementation of a smart agent-enabled system for personalizing wireless mobile services and advertisements for Java 2 Micro Edition (J2ME) or Java ME, and Wireless Application Protocol (WAP) enabled devices. We use software agents for context filtering because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment, and the Composite Capability/Preference Profiles (CC/PP) standard for defining profiles for user preferences and device capabilities. The system incorporates the use of artificial neural networks to adaptively and iteratively learn to select the best available service based on contextual information. The system is evaluated using practical operating scenarios, as well as empirical data and results show an 87% success rate in the selection of the best available service.     

Consensus evolution networks: A consensus reaching tool for managing consensus thresholds in group decision making

The consensus reaching process (CRP) is a critical part of group decision making (GDM). In order to explore the evolution of consensus, a new CRP tool is proposed based on consensus evolution networks (CENs). The CENs are built based on the consensus degrees among decision makers (DMs) and allow us to manage the consensus thresholds and its evolution. A new consensus index is introduced based on the structured and numerical aspects of the CENs. The new consensus index can not only deeply analyze the constitution of consensus, but also determine the weights of DMs. According to the clustering coefficient, the sensitive consensus threshold is identified and the sensitive consensus evolution network (SCEN) is built. Based on the complementary SCEN, a pairwise feedback adjustment method is proposed to improve consensus. Besides, the sparsity of the CENs can act as a reference to determine the agreed consensus thresholds, which is considered an important issue in traditional models. A numerical example is used to verify the usefulness of the proposed CRP tool. The numerical results show that the evolution of consensus can be clearly found based on CENs and the pairwise method can improve consensus in only four rounds.     

A novel group consensus protocol for heterogeneous multi-agent systems

In this paper, we consider the group consensus problem of heterogeneous multi-agent systems. Based on the feature of heterogeneous agents, a novel protocol is proposed for heterogeneous multi-agent systems. First, the state transformation method is used and an equivalent system is obtained. Then, the group consensus problem is analysed and some sufficient and/or necessary conditions are given for heterogenous multi-agent systems under undirected and directed networks, respectively. Finally, simulation examples are presented to demonstrate the effectiveness of the theoretical results.