人工智能在决策支持系统和专家系统中的作用

该文概述了专家系统(ES)与传统的决策支持系统(DSS)的定义与局限性,然后主要描述了人工智能(AI)在智能决策支持系统(IDSS)中的作用。     

人工智能在决策支持系统中的应用与研究

本文首先介绍了决策支持系统(DSS)的定义，针对其局限性提出了人工智能(AI)与决策支持系统的结合，然后分述了专家系统(ES)，机器学习(RL)，及智能体(Agent)在决策支持系统中的应用与研究。     

Managerial problem identifier (MPI): Integrating expert system and decision support technologies

In the past, managers have relied upon narrowly focused decision support systems (DSS) to facilitate the conventional decision process. Due to the human tendency to absorb only partial information, this technology has been underutilized. More contemporary theory advances the integration of Expert Systems (ES) with DSS to overcome the deficiencies found in conventional decision support systems. This paper presents a prototype Managerial Problem Identifier (MPI) that integrates decision support and expert systems. The interaction of financial accounting trends are analyzed by the expert system. The objective is to identify potential problems that might be responsive to early interaction.     

IDSS中专家系统的建造

智能决策支持系统（IDSS）是传统的决策支持系统与人工智能技术相结合的产物。这里使用C 来建造IDSS中的专家系统,将面向对象的方法用于专家系统的分析、设计和实现。经实际应用表明,能满足实际问题的需要,具有实用性和有效性。     

神经网络与专家系统结合的DSS模型智能构造

模型智能构造是目前IDSS发展的一个趋势.针对DSS模型构造中存在的问题,提出了一种神经网络与专家系统结合的DSS模型智能构造方案及其系统结构.在对文字描述和数据描述分离的基础上,应用专家系统实现模型类型的选择,应用神经网络实现模型结构的构造.以ARMA模型智能构造为例,说明了方法的可行性和有效性.     

基于知识的多专家决策支持系统

由于决策支持系统（ＤＳＳ）和专家系统（ＥＳ）的不同行为特征,使它们在信息处理中的决策和问题求解方面不能相互兼顾。本文交讨论和ＥＳ结合的可行性,提出一种基于知识的多专家决策支持系统的结构模式,及实现多专家协同求解和决策的方法。     

Evolution strategies – A comprehensive introduction

This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed.     

Change, attitude to change, and decision support system success

It is generally recognized that user attitudes are important determinants of success in the implementation of information systems. Among these, users' attitudes towards the changes introduced by a system are thought to be especially important in the implementation of MIS and OR/MS applications. Based on the results of a survey of 32 decision support systems (DSS), this study investigates the relationships between the extent of changes caused by the systems to users' work environments, the users' attitudes towards work related changes, and four measures of DSS success. The results indicate that DSS users are more satisfied with, and use more extensively, those DSS that bring change than DSS that do not result in substantial changes to their work environment. Implications for the measurement of DSS success are also discussed.     

An effective architecture for Decision Support Systems

Decision Support Systems (DSS) are, by nature, general- purpose systems, because they must support a variety of managers who have different decision styles and different problems. However, it seems that no effective general-purpose DSS have yet come into existence, although the components of DSS such as data base technology, modeling techniques, inexpensive graphic display etc., have progressed to the point where we should now be able to build effective DSS.This shortcoming seems to result from the following fact: Research on decision support has focused on data enlargement and model refinement, however, little attention has been paid to DSS architecture which integrates these components of DSS. It has not been well appreciated that DSS architecture itself facilitates learning about unstructured-problem solving and enables system evolution.In this paper, we propose a DSS architecture based on the study of unstructured-problem solving and considerations of the needs of managers as non-computer specialists. We illustrate this with a system realized using this architecture.     

The roles of user motivation to perform a task and decision support system (DSS) effectiveness and efficiency in DSS use

The purposes of this study are to: (1) obtain measures of actual decision support system (DSS) use that include the three elements of DSS use proposed by Burton-Jones and Straub (Burton-Jones, A., & Straub, D.W., Jr., (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246), and (2) identify an important psychological construct – a user’s motivation to perform a task – and examine how it interacts with two DSS characteristics – effectiveness and efficiency – to affect actual DSS use. As predicted, the findings indicated that individuals who used a more effective DSS to work on a task that they were motivated in increased usage of the DSS, while DSS use did not differ between individuals who used either a more or less effective DSS to complete a task that they were less motivated in. The results also showed significant difference for two measures of DSS use (i.e., STEP and TIME) and no significant difference for one measure of DSS use (i.e., USE) between individuals who used either a more or less efficient DSS to perform a task that they were more motivated in. As expected, significant differences were found for individuals who used either a more or less efficient DSS to complete a task that they were less motivated in. Finally, the results showed that DSS use increased when perceived ease of use and perceived usefulness of the DSS were high; therefore, these results corroborate the findings of prior research in the context of actual DSS use.     

Development of a spatial Decision Support System (DSS) for the Spencer Gulf penaeid prawn fishery,South Australia

The Spencer Gulf penaeid prawn fishery in South Australia has undergone a substantial increase in fishing efficiency (and profitability) mainly due to the implementation of adaptive harvest strategies requiring rapid response for change to harvesting plans. This paper describes the management background and the decision-making process leading to the development of a basic Decision Support System (DSS) that uses spatial information techniques and near real-time fishery-independent survey data. The system is implemented through linking an Oracle database to ArcGIS, Genstat and Splus. Two examples show the application of the DSS for optimal harvest timing and assessment of fishery sustainability. Fishery-independent survey data are used to assess stock and model population growth. The first example shows the information flow leading to a dynamic stock model and the estimate of value change as a function of harvest time. The second example shows how the DSS is used to validate and refine existing biological reference limits by evaluating long-term detailed data sets of the prawn population structure and catch dynamics. We conclude that it is important for the economic benefit and sustainability of the fishery to maintain and improve the collection of long-term data sets that are independent of commercial fishery statistics.     

Expert systems in the United Kingdom: an evaluation of development methodologies

Abstract: This paper discusses the results of a survey of ES producers conducted between October 1987 and June 1988 as part of a British Library Research and Development Department funded project. The survey aimed to establish the nature and scope of ES projects in Britain, the skills and methodologies used in their development and the background and experience of the personnel employed to develop them. A major part of the survey focused on the human input and organisational impact of ESs and this paper summarises the findings of the survey with particular reference to these issues. It includes details of the organisational background of those working in the area; a review of the nature, number and scope of ES projects in Britain; consultation procedures with both users and experts; the knowledge engineering task and methodologies currently used in undertaking it; the skills and attributes of the knowledge engineer; issues of updating and extending ESs; and hopes, projections and expectations for the future.     

Developing knowledge‐based decision support systems in public sector administration: A case study in the treasury department,ministry of finance,Malaysia

Since its introduction in the early 1970s the Decision Support System (DSS) concept has attracted a lot of attention from academics as well as users from business organizations. The literature abounds with accounts of systems that are claimed to have used the concept. The common characteristics of most of these systems are that they rely heavily on the use of quantitative models. In ill‐structured and higher level decision making quantitative models are used in conjunction with the qualitative models; in these cases a different approach and method in DSS development needs to be adopted. The objectives of this paper are twofold: to give a brief overview of the concept of DSS and Knowledge‐Based Systems and to describe the approach adopted and experience gained in conceptual analysis and modelling of the decision making process in an attempt to develop a Knowledge‐Based Decision Support System for Public Sector Management.     

煤巷锚杆支护设计专家决策支持系统

采用ＶＰ－ＥＸＰＥＲＴ和ＬＯＴＵＳ１－２－３分别构建专家系统（ＥＳ）和决策支持系统（ＤＳＳ）,并用ＤＯＳ批处理命令及ＶＰＢＣＡＬＬ将ＥＳ和ＤＳＳ连接建立成专家决策支持系统（ＥＤＳＳ）,可对煤巷锚杆支护设计进行咨询     

Meta web service: building web-based open decision support system based on web services

Web services are currently one of the trends in network-based business services, which intuitively will be applied to build a semantic web-based decision support system (DSS). Since web services are self-contained, modular business process applications, based on open standards, enable integration models for facilitating program-to-program interactions. Decision modules in a semantic web-based DSS can be viewed as a web service. However, according to the current features, web services know only about themselves, they are neither autonomous, nor are they designed to use ontologies; they are passive until invoked, and they do not provide for composing functionalities. These lead to the motivation on building a sophisticated web service to contain these features and to utilize web services on behalf of the user. This paper aims to propose a new concept of Meta Web Service, a web service-based DSS. The meta web service understands the user's problem statement with ontology, performs web service discovery, web service composition, and automatically generates codes for composite web service execution. Case-based reasoning is applied to quickly find past histories of successful service compositions. A prototype of research web service has been developed to show the feasibility of the proposed idea.     

A PC-based decision support and expert system for labour market analysis

Due to the worsening national economy, labour market analysis will receive increasing attention in the near future. This article describes an operating Decision Support (DSS) and Expert System (ES) for labour market analysis and its designing process. The $\text{DSS}\text{ES}$ is designed following the process of Pictorial Approach to modelling, and it provides researchers with timely and credible analysis of labour market information. This $\text{DSS}\text{ES}$ uses graphic presentation to delineate employment trends and magnitude, and it takes into consideration the special needs of business research centers by its implementations via spreadsheet application to improve the learning curve. By applying rule indices directly in the spreadsheet, the ES integrates with the DSS fully, and the decision makers are able to retain access to the historical data for ad hoc computations.     

Sensitivity analysis in logic-based models

An important feature of the causal models used in most DSS is that they allow their users to perform sensitivity analyses. Most of these analyses are performed on continuous variables, which are the principal types of variables found in causal models. However, some of the models derived from artificial intelligence that are increasingly being used in DSS contain logic (or Boolean) variables. In this paper we exploit the notion of a ‘Boolean derivative’ developed for circuit design and apply it to the calculation of sensitivity measures in logic-based models.     

Model-based software configuration: patterns and languages

The common presupposition of enterprise systems (ES) is that they lead to significant efficiency gains. However, this is only the case for well-implemented ES that are well-aligned with the organisation. The list of ES implementation failures is significant which is partly attributable to the insufficiently addressed fundamental problem of adapting an ES efficiently. As long as it is not intuitively possible to configure an ES, this problem will prevail because organisations have a non-generic character. A solution to this problem consists in re-thinking current practices of ES provision. This paper proposes a new approach based on configurable process models, which reflect ES functionalities. We provide in this paper a taxonomy of situations that can occur from a business perspective during process model configuration. This taxonomy is represented via so-called semantic configuration patterns. In the next step, we discuss so-called syntactic configuration patterns. This second type of configuration patterns implements the semantic configuration patterns for specific modelling techniques. We chose two popular process modelling languages in order to illustrate our approach.     

On the network impact of dynamic server selection

Widespread replication of information can ameliorate the problem of server overloading but raises the allied question of server selection. Clients may be assigned to a replica in a static manner or they may choose among replicas based on client-initiated measurements. The latter technique, called dynamic server selection (DSS), can provide significantly improved response time to users when compared with static server assignment policies (for example, based on network distance in hops).In the first part of this paper we demonstrate the idea of DSS using experiments performed in the Internet. We compare a range of policies for DSS and show that obtaining additional information about servers and paths in the Internet before choosing a server improves response time significantly. The best policy we examine adopts a strategy of never adding more than 1% additional traffic to the network, and is still able to provide nearly all the benefits of the most expensive policies.While these results suggest that DSS is beneficial from the network user's standpoint, the system-wide effects of DSS schemes should also be closely examined. In the second part of this paper we use large-scale simulation to study the system-wide network impact of dynamic server selection. We use a simulated network of over 100 hosts that allows local-area effects to be distinguished from wide-area effects within traffic patterns.In this environment we compare DSS with static server selection schemes and confirm that client benefits remain even when many use DSS simultaneously. Importantly, we also show that DSS confers system-wide benefits from the network standpoint, as compared to static server selection. First, overall data traffic volume in the network is reduced, since DSS tends to diminish network congestion. Second, traffic distribution improves – traffic is shifted from the backbone to regional and local networks.