GROUP DECISION-MAKING WITH A FUZZY MAJORITY VIA LINGUISTIC QUANTIFIERS. PART II: A COMPETITIVE-LIKE POOLING

Abstract

Starting from individual fuzzy preference relations, some (sets of) socially best acceptable options are determined, directly or via a social fuzzy preference relation. An assumed fuzzy majority rule is given by a fuzzy linguistic quantifier, e.g., “most.” Here, as opposed to Part I, where we used a consensory-like pooling of individual opinions, we use an approach to linguistic quantifiers that leads to a competitive-like pooling. Some solution concepts are considered: cores, minimax (opposition) sets, consensus winners, and so forth,     

Flexible Web services integration: a novel personalised social approach

Abstract

Dynamic composition or integration remains one of the key objectives of Web services technology. This paper aims to propose an innovative approach of dynamic Web services composition based on functional and non-functional attributes and individual preferences. In this approach, social networks of Web services are used to maintain interactions between Web services in order to select and compose Web services that are more tightly related to user’s preferences. We use the concept of Web services community in a social network of Web services to reduce considerably their search space. These communities are created by the direct involvement of Web services providers.     

On the cardinality index and attitudinal character of fuzzy measures

We briefly introduce the fuzzy measure and then discuss its use in representing information about uncertain variables. A relationship between the fuzzy measure and the Dempster-Shafer belief structure is discussed and a method for generating the family of fuzzy measures associated with a belief structure is described. We discuss the use of the Shapley index as a means for introducing an extension of the concept of entropy to fuzzy measures called the Shapley entropy. It is shown that all fuzzy measures generated from a given Dempster-Shafer belief structure have the same value for their Shapley entropy. We introduce the cardinality index of a fuzzy measure and use it to define the attitudinal character of a fuzzy measure. A semantics for this attitudinal character in the framework of using fuzzy measures to represent information about uncertain variables is suggested.     

The Design of Communicative Act Libraries: A Linguistic Perspective

The FIPA Agent Communication Language includes a library of communicative acts supporting generic types of social interaction, such as information exchange and action performing. However, when developing specific applications, it is desirable to support agent interaction by means of more expressive constructs. In this paper, a structured approach to the design of communicative act libraries is put forward, which takes into account both the criteria of reusability and expressiveness. A linguistic approach is taken to ensure the expressiveness requirement. The proposal is illustrated by the design of an advisory sub-catalogue of reusable communicative acts.     

FuzzyQoI model: A fuzzy logic-based modelling of users' quality of interaction with a learning management system under blended learning

Learning management systems (LMSs) in higher education institutions (HEIs) provide the potential for rich learning environments built on social constructivist theories under the concept of blended (b-)learning. An essential factor, however, in determining the efficacy of online teaching–learning is the users' quality of interaction (QoI) with LMSs; yet, in many cases, QoI has not been properly acquired, mainly, due to its inherent qualitative character. Stemming from the latter, this study introduces a new model, namely FuzzyQoI, that, by employing fuzzy logic constructs, it quantitatively estimates the users' (professors' and students') QoI with the LMS Moodle within a b-learning environment. In the FuzzyQoI model, a set of 110 LMS Moodle metrics is considered to form 12 codified inputs to a five-level fuzzy inference system equipped with 600 expert's fuzzy rules. The potential and effectiveness of the realisation of the FuzzyQoI in practice are evaluated from its trialling on LMS data drawn from a large users' database (75 professors and 1037 students), referring to a five-course b-learning process of 51 weeks at a HEI. Experimental results have shown that the proposed FuzzyQoI model efficiently identified (dis)similarities in LMS interaction trends, correlations, distributions and dependencies with the time-period of the LMS use, both for the user-dependent and user-independent (group-like) cases. Consequently, the proposed FuzzyQoI functions as a means for better understanding and explaining the nature of underlying aspects, which influence the users' interaction behaviour under the LMS-based b-learning approach.     

Stability analysis of fuzzy large-scale systems with time delays in interconnections

The stability problem of fuzzy large-scale systems with time delays in interconnections is considered in this paper. The fuzzy large-scale system consists of J interconnected subsystems, which are represented by Takagi-Sugeno (T-S) fuzzy models. The stability conditions are derived using the Lyapunov-Krasovskii functional approach. This condition is independent of time delay and does not need the solution of a Lyapunov equation or Riccati equation. A stabilization approach for the delayed fuzzy large-scale systems through fuzzy state feedback-based controller is also presented in this paper. Finally, an example is given to demonstrate the result.     

Planning for tourism routes using social networks

Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the minube¹ traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube, groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

Learning to annotate via social interaction analytics

Recent years have witnessed increased interests in exploiting automatic annotating techniques for managing and retrieving media contents. Previous studies on automatic annotating usually rely on the metadata which are often unavailable for use. Instead, multimedia contents usually arouse frequent preference-sensitive interactions in the online social networks of public social media platforms, which can be organized in the form of interaction graph for intensive study. Inspired by this observation, we propose a novel media annotating method based on the analytics of streaming social interactions of media content instead of the metadata. The basic assumption of our approach is that different types of social media content may attract latent social group with different preferences, thus generate different preference-sensitive interactions, which could be reflected as localized dense subgraph with clear preferences. To this end, we first iteratively select nodes from streaming records to build the preference-sensitive subgraphs, then uniformly extract several static and topologic features to describe these subgraphs, and finally integrate these features into a learning-to-rank framework for automatic annotating. Extensive experiments on several real-world date sets clearly show that the proposed approach outperforms the baseline methods with a significant margin.     

CUSTOMIZING THE INSTRUCTIONAL GRID

The Web contains hundreds of thousands of educational resources available any time and any place. However no smart technology is available to help teachers and students locate appropriate resources customized to their needs and social characteristics. When educational resources are indexed, it is often done by demographics, such as student age and grade. This article describes customized Grid Learning Services (GLS) that will personalize instruction based on an individual's presumed knowledge and cognitive and learning needs. The customized GLS will use real-time student modeling, the Semantic Web, intelligent agents, and pre-tests of cognitive, affective, and social characteristics to personalize the selection of educational resources and problems. Components of the customized GLS include an ontology construction agent, goal-based retrieval mechanisms, a lesson planner, and student and pedagogical agents.     

FRACTAL GEOMETRY: A TOOL FOR FUZZY REASONING

Abstract

A new procedure for construction of a fuzzy number is given, based on the covering of points by consequently contracting intervals. The approach leads to the membership function in the form of Devil's staircase, a borderline fractal finding many applications in the modern physics. The alternative definition of subsethood based on the self-similarity of the sets is also given.     

Toward automated feature model configuration with optimizing non-functional requirements

ContextA software product line is a family of software systems that share some common features but also have significant variabilities. A feature model is a variability modeling artifact, which represents differences among software products with respect to the variability relationships among their features. Having a feature model along with a reference model developed in the domain engineering lifecycle, a concrete product of the family is derived by binding the variation points in the feature model (called configuration process) and by instantiating the reference model.ObjectiveIn this work we address the feature model configuration problem and propose a framework to automatically select suitable features that satisfy both the functional and non-functional preferences and constraints of stakeholders. Additionally, interdependencies between various non-functional properties are taken into account in the framework.MethodThe proposed framework combines Analytical Hierarchy Process (AHP) and Fuzzy Cognitive Maps (FCM) to compute the non-functional properties weights based on stakeholders’ preferences and interdependencies between non-functional properties. Afterwards, Hierarchical Task Network (HTN) planning is applied to find the optimal feature model configuration.ResultOur approach improves state-of-art of feature model configuration by considering positive or negative impacts of the features on non-functional properties, the stakeholders’ preferences, and non-functional interdependencies. The approach presented in this paper extends earlier work presented in [1] from several distinct perspectives including mechanisms handling interdependencies between non-functional properties, proposing a novel tooling architecture, and offering visualization and interaction techniques for representing functional and non-functional aspects of feature models.Conclusionour experiments show the scalability of our configuration approach when considering both functional and non-functional requirements of stakeholders.     

Machining process planning of prismatic parts using case-based reasoning and past process knowledge

A process planner for three-dimension prismatic parts is developed in this paper by utilizing case-based techniques. A three-dimension prismatic part is represented by a set of primary features (such as holes, pockets, slots, etc). The subplan candidates for individual features of a part are first generated by the proposed system via a backward inference planner based on the specifications of cutting tools available in a factory. The system then combines all subplans into the final process plan for a given part based on the merging information. The merging information is the information regarding the manufacturing environment of a factory (i.e. machine layout, transfer line, etc), and plays a key role in the process planning. Generally, the merging information is contained in old plans, and will be extracted by the system using case-based techniques. This way, the proposed system can generate a practical process plan for a given part based on case histories provided by the factory itself. The proposed process planner is composed of five major components: feature indexer, retriever, modifier, simulator, and repairer. It is implemented on a Sun workstation using the ACIS geometric modeler and C++ .     

Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system

ObjectiveTo develop a classifier that tackles the problem of determining the risk of a patient of suffering from a cardiovascular disease within the next 10 years. The system has to provide both a diagnosis and an interpretable model explaining the decision. In this way, doctors are able to analyse the usefulness of the information given by the system.MethodsLinguistic fuzzy rule-based classification systems are used, since they provide a good classification rate and a highly interpretable model. More specifically, a new methodology to combine fuzzy rule-based classification systems with interval-valued fuzzy sets is proposed, which is composed of three steps: (1) the modelling of the linguistic labels of the classifier using interval-valued fuzzy sets; (2) the use of the K_α operator in the inference process and (3) the application of a genetic tuning to find the best ignorance degree that each interval-valued fuzzy set represents as well as the best value for the parameter α of the K_α operator in each rule.ResultsThe suitability of the new proposal to deal with this medical diagnosis classification problem is shown by comparing its performance with respect to the one provided by two classical fuzzy classifiers and a previous interval-valued fuzzy rule-based classification system. The performance of the new method is statistically better than the ones obtained with the methods considered in the comparison. The new proposal enhances both the total number of correctly diagnosed patients, around 3% with respect the classical fuzzy classifiers and around 1% vs. the previous interval-valued fuzzy classifier, and the classifier ability to correctly differentiate patients of the different risk categories.ConclusionThe proposed methodology is a suitable tool to face the medical diagnosis of cardiovascular diseases, since it obtains a good classification rate and it also provides an interpretable model that can be easily understood by the doctors.     

Fuzzy semantic tagging and flexible querying of XML documents extracted from the Web

The relational database model is widely used in real applications. We propose a way of complementing such a database with an XML data warehouse. The approach we propose is generic, and driven by a domain ontology. The XML data warehouse is built from data extracted from the Web, which are semantically tagged using terms belonging to the domain ontology. The semantic tagging is fuzzy, since, instead of tagging the values of the Web document with one value of the domain ontology, we propose to use tags expressed in terms of a possibility distribution representing a set of possible terms, each term being weighted by a possibility degree. The querying of the XML data warehouse is also fuzzy: the end-users can express their preferences by means of fuzzy selection criteria. We present our approach on a first application domain: predictive microbiology.     

An Agent Model for the Resolution of Feature Conflicts in Telephony

Most telecommunication service providers resolve the feature interaction problem by providing specific instructions in their management software to handle scenarios where feature interaction may occur. This approach suffers from the complexity of the resulting code and the difficulty of adding new features to the system. Moreover, the system predefines the result of the resolution of the conflicting features and the end user has no means of choosing a different behavior, depending on the preferences of the user. In this paper we propose an agent-based architecture to detect and resolve feature interactions. Our system benefits from the flexibility and the semantic richness of policies and fuzzy logic to allow the end user to alter the behavior of the system, thus obtaining a more personalized service.     

模糊熵的区域合并和图割的快速矿岩分割

目的 针对现有区域合并和图割的结合算法没有考虑矿岩图像模糊特性,导致分割精度和运行效率较低,模糊边缘无法有效分割的问题,利用快速递推计算的最大模糊2-划熵信息设置以区域为顶点的图割模型似然能来解决。方法 首先利用双边滤波器和分水岭算法对矿岩图像进行预处理,并将其划分为若干一致性较好的区域;然后利用图像在计算最大模糊2-划分熵时,目标和背景的模糊隶属度函数来设计图割能量函数似然能,使得能量函数更接近模糊图像的真实情况,期间为了提高最大模糊2-划分熵值的搜索效率,提出了时间复杂度为O（n²）的递推算法将模糊熵的计算转化为递推过程,并保留不重复的递推结果用于后续的穷举搜索;最后利用设计的图割算法对区域进行标号,以完成分割。结果 本文算法的分割精度较其他区域合并和图割结合算法提高了约23%,分割后矿岩颗粒个数的统计结果相对于人工统计结果,其误差率约为2%,运行时间较其他算法缩短了约60%。结论 本文算法确保精度同时,有效提高矿岩图像的分割效率,为自动化矿岩图像高效分割的工程实践提供重要指导依据。     

Acquiring and sharing tacit knowledge in software development teams: An empirical study

ContextSharing expert knowledge is a key process in developing software products. Since expert knowledge is mostly tacit, the acquisition and sharing of tacit knowledge along with the development of a transactive memory system (TMS) are significant factors in effective software teams.ObjectiveWe seek to enhance our understanding human factors in the software development process and provide support for the agile approach, particularly in its advocacy of social interaction, by answering two questions: How do software development teams acquire and share tacit knowledge? What roles do tacit knowledge and transactive memory play in successful team performance?MethodA theoretical model describing the process for acquiring and sharing tacit knowledge and development of a TMS through social interaction is presented and a second predictive model addresses the two research questions above. The elements of the predictive model and other demographic variables were incorporated into a larger online survey for software development teams, completed by 46 software SMEs, consisting of 181 individual team members.ResultsOur results show that team tacit knowledge is acquired and shared directly through good quality social interactions and through the development of a TMS with quality of social interaction playing a greater role than transactive memory. Both TMS and team tacit knowledge predict effectiveness but not efficiency in software teams.ConclusionIt is concluded that TMS and team tacit knowledge can differentiate between low- and high-performing teams in terms of effectiveness, where more effective teams have a competitive advantage in developing new products and bringing them to market. As face-to-face social interaction is key, collocated, functionally rich, domain expert teams are advocated rather than distributed teams, though arguably the team manager may be in a separate geographic location provided that there is frequent communication and effective use of issue tracking tools as in agile teams.     

考虑专家偏好关联的群决策方法及其应用

通过分析群决策过程,提出使用模糊测度描述专家偏好之间可能存在的关联关系,并给出了一种考虑专家偏好关联的群决策方法.该方法从参评专家知识结构的相似性及判断结果的相似性出发,通过计算得到相应的2-可加模糊测度来描述专家的重要程度,并使用Choquet积分将多个专家的偏好信息聚合为群体的判断结果.最后,通过一个潜艇装备论证的例子验证了所提出方法的可行性和合理性.     

Fuzzy Relations in Group Decision Theory

Abstract

This paper develops the notion of fuzzy preference orderings for individuals and groups, on the basis of the theory of fuzzy (binary) relations as developed by L. A. Zadeh [4]. This concept is illustrated with well known methods of group decision-making and some of its formal properties are investigated. Finally an algorithm to map fuzzy orderings on non-fuzzy ones is proposed to guide the decision-maker in reaching decisions which reflect the fuzziness of the preferences over the available options.