An ontology-based support for product conceptual design

The research presented in this paper is a follow-up of our prior work involving the development of a graphical modeling tool to support designers at the conceptual design stage. To close the loop for supporting designers in generating design concepts flexibly, fast, and easily, an ontology-based approach for knowledge management that works along with the graphical modeling tool is discussed. Ontology and databases for the tool are developed to promote the systematic capture of design knowledge and efficient reuse of the design knowledge selection. In order to locate the proper information and query the data from the databases, the relationship between the ontology and databases, the data analysis process, ontology enrichment, and the ontology-based query engine are built to offer users multiple design results according to users’ requirements. A tire design example is presented to demonstrate the proposed approach. The ontology-based method described in this paper can help retrieve and save the complex relations, support the reasoning, integrate heterogeneous data resources and offer users more accurate, proper and comprehensive data.     

An ontology-based fuzzy decision support system for multiple sclerosis

The use of Magnetic Resonance (MR) as a supporting tool in the diagnosis and monitoring of multiple sclerosis (MS) and in the assessment of treatment effects requires the accurate determination of cerebral white matter lesion (WML) volumes. In order to automatically support neuroradiologists in the classification of WMLs, an ontology-based fuzzy decision support system (DSS) has been devised and implemented. The DSS encodes high-level, specialized medical knowledge in terms of ontologies and fuzzy rules and applies this knowledge in conjunction with a fuzzy inference engine to classify WMLs and to obtain a measure of their volumes. The performance of the DSS has been quantitatively evaluated on 120 patients affected by MS. Specifically, binary classification results have been first obtained by applying thresholds on fuzzy outputs and then evaluated, by means of ROC curves, in terms of trade-off between sensitivity and specificity. Similarity measures of WMLs have been also computed for a further quantitative analysis. Moreover, a statistical analysis has been carried out for appraising the DSS influence on the diagnostic tasks of physicians. The evaluation has shown that the DSS offers an innovative and valuable way to perform automated WML classification in real clinical settings.     

一种基于本体的P2P网络搜索方法

现有P2P网络规模大、动态性高、异构性强.有效的搜索技术一直是P2P系统研究中的核心问题.本文针对无结构P2P网络泛洪搜索机制的盲目性所导致的查询开销大、效率低的问题,提出了一种基于本体的P2P网络搜索方法OA.该方法给出了一个基于本体的P2P网络搜索模型,并设计了一个全局共享的节点本体来表示和组织节点知识,以提高检索质量.同时,设计了一个节点聚类算法PCA来改善消息路由.实验结果表明,OA算法比Gnutella算法实现了更高的查准率和查全率.     

Computer support for strategic organizational decision-making

While information systems continue to be promoted within organizations as tools to support strategic decision-making, there is growing concern over the ability of such systems to model the social and political complexity of the situations to which they are being applied. This paper examines the nature of organizational decision-making and the use of computer-based systems to support this activity. The debate queries the extent to which such artifacts should be allowed to become enmeshed and embedded within the strategic decision-making activities of organizations which operate within increasingly complex environments.     

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.     

Machine learning-based consensus decision-making support for crowd-scale deliberation

Applied Intelligence - With the rapid development of Internet, the online discussion system or social democratic system has become an important and effective vehicle for group decision-making...     

Multi-agent system to support decision-making process in design for recycling

In this paper, the application of multi-agent system to support decision-making process in design for recycling is presented. The design for recycling term is highlighted either as the design problem or from the point of view of regulations. The structure of agent system supporting the designer during the design process is showed. The basis of special kind of product model, that is the extension of standard 3D product model, called recycling-oriented product model, is described. The example results of analysis, based on real household appliance model, are presented.     

Information technology in support of individual decision-making

Abstract. The use of computers in organizations has often been justified by reference to the ‘improved decision-making’ which will result from the use of new technology. Rarely, if ever, is any precise measurement given as to how such improvements will be judged. Phrases such as ‘better information leads to better decisions’ and ‘what managers require is more information’ dominate the literature on management information systems (MIS). Alistair Preston argues that this alignment with top management allowed MIS to legitimize and elevate itself as both a serious practice and an academic discipline. The two dominant themes in the relationship between information technology (IT) and managerial decision-making have been the use of Herbert Simon's work on decision-making and Anthony's pyramidal structure of types of application system, the latter model leading to a powerful categorization of types of computer-based information systems (i.e. transaction processing, management information systems, decision support systems, executive information systems). The models of decision-making employed at each level of this hierarchy are those characterized by Simon as ‘objective rationality’ and ‘bounded rationality’ and these models encourage a view of information as representing some objective reality which can be captured, stored and processed within some form of technology. Dick Boland has argued forcibly that Simon's work has been crucial to the current level of understanding of information and technology within organizations. Less concern has been given, however, to the role which IT itself plays in reinforcing this rationalistic view of information and decision-making. As Mitroff has pointed out, to represent (or model) a problem is to conduct an inquiry into its nature. Likewise, Dick Boland suggests that information technologies are themselves social inventions that are malleable and shaped during use but that such sets limits and opens possibilities that shape the users as well. Hence, information technologies both incorporate and give rise to changes in our vocabularies for knowing ourselves and our institutions. It will not be enough, therefore, for us merely to change our existing models of managerial decision-making, it will also be necessary to design new technologies which will reflect and encourage the use of such models. In this paper we shall use the framework provided by Mitroff to try to consider a number of different paradigms of inquiring system and to see what technological forms would need to be developed in order that we may provide technological support for individual decision-making based on each paradigm.     

农田远程智能诊断与决策系统研究与实现

为了更加有效地辅助农业生产，在综合了地理信息系统（GIS）与专家系统（ES）优势的基础之上，将系统分析技术、网络技术、知识表达技术、系统集成技术与数据库技术结合起来设计开发了农田诊断与决策系统。该系统在北京顺义地区小麦的生产中得到应用，对指导小麦的生产和管理，提高小麦产量和质量起到了重要作用，得到了用户的好评。     

An ontology-based analysis and semantics for organizational structure modeling in the ARIS method

This paper focuses on the issue of ontological analysis for organizational structure modeling in the ARIS method with the following contributions: (i) an interpretation of the language in terms of real-world entities in the UFO foundational ontology; (ii) the identification of inappropriate elements of the language, using a systematic ontology-based analysis approach; and (iii) recommendations for improvements of the language to resolve the issues identified.     

A formal framework for scenario development in support of environmental decision-making

Scenarios are possible future states of the world that represent alternative plausible conditions under different assumptions. Often, scenarios are developed in a context relevant to stakeholders involved in their applications since the evaluation of scenario outcomes and implications can enhance decision-making activities. This paper reviews the state-of-the-art of scenario development and proposes a formal approach to scenario development in environmental decision-making. The discussion of current issues in scenario studies includes advantages and obstacles in utilizing a formal scenario development framework, and the different forms of uncertainty inherent in scenario development, as well as how they should be treated. An appendix for common scenario terminology has been attached for clarity. Major recommendations for future research in this area include proper consideration of uncertainty in scenario studies in particular in relation to stakeholder relevant information, construction of scenarios that are more diverse in nature, and sharing of information and resources among the scenario development research community.     

A decision-making method for personalized composite service

Web services are emerging technologies that enable application to application communication and reuse of autonomous services. Web services composition is a concept of integrating component services to conduct complex business transactions based on functionality and performance constraints. With the rapid growth of Web services on the Internet, the services owing the same functionality and different performance become more and more, and different users care for different quality. Therefore, designing effective decision-making method for the personalized composite service has become a fundamental problem for the application based on Web service. In this paper, oriented to the user need with single optimization objective, an efficient algorithm, which consists of Particle Swarm Optimization (PSO) and Niche technology, is presented to solve the Web service selection problem. Furthermore, in view of the practical composition requirements including multiple optimization objectives, an algorithm used to resolve the service selection with multi-objective multi-constraint is designed based on NPSO and the intelligent optimization theory of multi-objective PSO, which can produce a set of Pareto optimal composite services by means of optimizing various objective functions simultaneously. Experimental results show that NPSO algorithm owns better global convergence ability with faster convergence speed. Meanwhile, multi-objective multi-constraint NPSO is both feasible and efficient.     

A FTA-based method for risk decision-making in emergency response

Decision-making problems in emergency response are usually risky and uncertain due to the limited decision data and possible evolvement of emergency scenarios. This paper focuses on a risk decision-making problem in emergency response with several distinct characteristics including dynamic evolvement process of emergency, multiple scenarios, and impact of response actions on the emergency scenarios. A method based on Fault Tree Analysis (FTA) is proposed to solve the problem. By analyzing the evolvement process of emergency, the Fault Tree (FT) is constructed to describe the logical relations among conditions and factors resulting in the evolvement of emergency. Given different feasible response actions, the probabilities of emergency scenarios are estimated by FTA. Furthermore, the overall ranking value of each action is calculated, and a ranking of feasible response actions is determined. Finally, a case study on H1N1 infectious diseases is given to illustrate the feasibility and validity of the proposed method.     

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.     

基于AHP的群决策风险评估方法

运用层次分析法对一信息系统进行风险评估,以风验发生的概率、风险的影响、风险的不可控性作为层次模型中准则层的元素,采用群决策的方式确定风险因素相对于准则层各元素的层次单排序,专家的权重由其所构造的判断矩阵与其他矩阵之间的相似程度来确定,然后采用加权几何平均的算法将专家的判断聚合为一个共识度矩阵.     

Value prediction in hls allocation problems using intellectual properties

A value approximation-based global search algorithm is suggested to solve resource-constrained allocation in high level synthesis problems. Value approximation is preferred, because it can start by using expert heuristics, can estimate the global structure of the search problem, and can optimize heuristics. We are concerned by those allocation problems that have hidden global structure that value approximation may unravel. The value approximation applied here computes the cost of the actual solution and estimates the cost of the solution that could be achieved upon performing a global search on the hidden structure starting from the actual solution. We transcribed the allocation problem into a special form of weighted CNF formulae to suit our approach. We also extended the formalism to pipeline operations. Comparisons are made with expert heuristics. Scaling of computation time and performance are compared.     

A new methodology to support group decision-making for IoT-based emergency response systems

An emergency response system (ERS) can assist a municipality or government in improving its capabilities to respond urgent and severe events. The responsiveness and effectiveness of an ERS relies greatly on its data acquisition and processing system, which has been evolved with information technology (IT). With the rapid development of sensor networks and cloud computing, the emerging Internet of things (IoT) tends to play an increasing role in ERSs; the networks of sensors, public services, and experts are able to interact with each other and make scientific decisions to the emergencies based on real-time data. When group decision making is required in an ERS, one critical challenge is to obtain the good understanding of massive and diversified data and make consensus group decisions under a high-level stress and strict time constraint. Due to the nature of unorganized data and system complexity, an ERS depends on the perceptions and judgments of experts from different domains; it is challenging to assess the consensus of understanding on the collected data and response plans before appropriate decisions can be reached for emergencies. In this paper, the group decision-making to emergency situations is formulated as a multiple attribute group decision making (MAGDM) problem, the consensus among experts is modeled, and a new methodology is proposed to reach the understanding of emergency response plans with the maximized consensus in course of decision-making. In the implementation, the proposed methodology in integrated with computer programs and encapsulated as a service on the server. The objectives of the new methodology are (i) to enhance the comprehensive group cognizance on emergent scenarios and response plans and (ii) to accelerate the consensus for decision making with an intelligent clustering algorithm, (iii) to adjust the experts’ opinions without affecting the reliability of the decision when the consensus cannot be reached from the preliminary decision-making steps. Partitioning Around Medoids (PAM) has been applied as the clustering algorithm, Particle Swarm Optimization (PSO) is deployed to adjust evaluation values automatically. The methodology is applied in a case study to illustrate its effectiveness in converging group opinions and promoting the consensus of understanding on emergencies.     

Unbalanced probabilistic linguistic decision-making method for multi-attribute group decision-making problems with heterogeneous relationships and incomplete information

Artificial Intelligence Review - In group decision-making problems, decision makers prefer to use several linguistic terms to describe their own perception and knowledge, and give their preference...     

MM-DSS: Integrating multimedia and decision-making knowledge in decision support systems

Multimedia (MM) information systems have been utilized for decision-making tasks since they were developed in the early 1990s. Few research studies to date have paid attention to the integrated mode of MM information with a decision support system (DSS) or have investigated the role of MM information and DSS from the perspective of model-based knowledge management. This paper proposes a multimedia decision support system (MM-DSS) to address the above unexplored issues. The proposed MM-DSS manages MM information with the use of a newly developed extended attributed relational graph (ARG) and the structured modeling (SM) technique in order to support complicated decision-making processes intelligently and systematically. This paper illustrates the development of the new extended ARG and the SM method, and their integration into mathematical decision-making knowledge. The proposed MM-DSS responds to the needs of representation, storage, and retrieval of MM information in decision-making processes. Results of simulation show that the proposed MM-DSS is successful in tackling an optimization problem of factory scheduling.