Knowledge engineering concepts for decision support system design and implementation

The design of computer-based systems that simulate expert human consulting by drawing on large amounts of task-specific knowledge has been a major research activity of applied artificial intelligence over the last ten years. Building decision support systems that incorporate aspects of this research is a promising new field. The purpose of this paper is to discuss concepts of “knowledge engineering” that are most relevant in designing and building knowledge-based decision support systems.     

A new class of intelligent knowledge-based systems with an optimisation-based inference engine

In this paper we describe a new class of intelligent knowledge-based system (IKBS) which can be used principally for managerial decision making applications. This class of applications often requires a framework for knowledge acquisition which allows the system to use the knowledge of several experts. In addition, since in most business decision making the objective is maximise profits, there is a need for an inference engine which allows optimisation to be carried out. The new class of IKBS which is described in this paper has both these properties, i.e., the ability to use the knowledge of multiple experts in a convenient way and an inference engine which by performing optimisations is able to pick out the profit maximising decisions. As an illustration of these concepts, a system for allocation decision making is described. The system ‘Retail-opt’ allows the user to solve problems like allocation of space in retail outlets, allocation of space in mail order catalogues, pricing policy decisions for discounted airline tickets, etc. In the paper, the basic concepts behind ‘Retail-opt’ are described and an application of ‘Retail-opt’ to the problem of retail space allocation in a Scandinavian Department Store is given. A number of other systems which use these concepts for more complicated competitive decision making situations are also described.     

An examination of decision-makers' utilization of decision support system output

A study was conducted of 18 decision support systems in major U.S. corporations in order to examine their methods of operation and the ways in which the DSS contribute to the decision making process. These DSS were studied with respect to organizational level of the decision maker, phases of the decision making process, interaction among decision makers using the DSS, requirement for and regularity of DSS use, impact on job tasks and performance, and perceived value of the DSS. Study results provide substantial support for generalized conceptualizations in the literature. Decision support is primarily for upper and/or middle management working in interaction. Use of the DSS is at the discretion of the decision maker and the majority of DSS users are highly satisfied with their systems.     

Analytic hierarchy process: It can work for group decision support systems

Most group problems are complex and unstructured and are difficult to find solutions, GDSS, systems for group decision making support, have more concentrated on the development of technological factor such as communication support, electronic meeting support, etc. And there have been relatively few researches about group problem modeling and group decision-aid. We cannot guarantee the value of GDSS just with the support in technological basis. The AHP has been suggested as an group problem modeling tool. In this paper authors applied AHP in real world group problems, investigated its value. and suggested the elements of GDSS software.     

Problem-solving strategies for DSS design

For a successful design and implementation of DSS, competent understanding of decision-making processes in organizational settings and sensitivity to the interpersonal and organizational dimensions of the relationship between decision-makers and DSS designers are essential. Towards this end, a three-faceted strategy is broadly outlined and proposed. The strategy encompasses (a) a diagnostic attitude to the empirical tasks and contextual properties of decision-making, (b) emphasis on decision-makers rather than on decisions, and (c) the concept of DSS design as a joint undertaking for organizational problem-solving.     

The development of decision support systems in China

The short history of development of Decision Support Systems (DSS) in China is described. Some examples of DSS are illustrated. And four main directions involved in the DSS development are pointed out.     

Production decision support system for computerized manufacturing systems

The introduction of Computerized Manufacturing Systems (CMS) into the mid-volume manufacturing industry has been an important new step in the development of automated manufacturing. CMS's are computer-integrated systems which incorporate Direct Numerical Control (DNC) capabilities with automatic material handling to manufacture parts. Due to its complex nature, the task of production control in an CMS cannot rely on human effort alone. It is essential to develop a software package in the form of a Production Decision Support System (PDSS) to assist the production decision maker in operating this complex manufacturing facility.     

A view of group decision making process and bivoting approach

Many characteristics of Group Decision Making (GDM) are different from those of individual decision. The literature dealing with the stages of GDM is rather scant. This paper presents a view about the stages of group decision processes, characterizing GDM as a dynamic process. We discuss particularly the aggregation stage of preferences and reports a bivoting approach' used in practising group decision support systems (GDSS). Finally, a concrete example is given to illustrate each stage of group decision process and tile 'bivoting approach' in consensus reaching stage.     

基于信息熵与证据推理的不确定多属性决策方法

针对不确定多属性决策过程中信息特征呈多样化且属性权重完全未知时难以进行决策的问题,提出了一种基于不确定信息熵的权值计算方法。该方法首先将各种不确定信息转换到统一的信度框架下,计算出不确定信息熵从而获得属性客观权重值。该方法结合证据推理法可实现属性权重未知时不确定信息下的多属性决策。最后以应用实例验证了该方法的有效性和可行性。     

Multiknowledge for decision making

The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.     

Value-driven expert systems for decision support

Values are an inherent part of all decision processes. Hence, values are at least implicity included in all expert systems intended for decision support. This paper outlines the concepts and methodology, which are based on the principles and procedures of decision analysis, to address explicity the values in an expert system logically and consistently. Implementation of the concepts and methodology involves the elicitation of values using the same general approach as that used by knowledge engineers to explicate expert knowledge.     

Design of a decision support system for detailed scheduling

Design of a decision support system ( ) based on a simulation model of the detailed scheduling activities in a tractor manufacturing company is dealt with. The system analysis phase of the design process is overviewed briefly. The main decision points involved and the problems faced in the production planning and control subsystem are presented. Expectations from a for detailed scheduling are discussed and performance measures are defined. The links between computer programs are shown. Utilization of the designed for production planning and control oriented decision making is discussed using decision tables.     

Integrating and navigating engineering design decision-related knowledge using decision knowledge graph

Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge.     

Retrieval for decision support resources by structured models

The number of available DSS within organizational Intranets will soon require efficient retrieval functionality. While Web retrieval technology performs excellent on documents, computational services need approaches that capture the semantics of resources. We present a retrieval approach that uses a variant of Structured Modeling to represent resources. It allows the use of similarity of models for retrieval. Exact similarity computation is shown to be NP-hard, and efficient heuristics for similarity computation and filter algorithms are introduced. We report an evaluation in a classroom experiment and give computational results on a benchmark library.     

Adapting demons to knowledge management environments

Decision support systems depend on a variety of knowledge management techniques. These range from data base management, programming, and spreadsheet analysis to rule set management and automated inference. One valuable knowledge management technique that has yet to find its way into the repertoire of decision support system developers is general-purpose demon management. This article identifies and explores the major issues pertaining to the integration of demon representation and processing into a knowledge management environment. These serve as a basis for design and implementation of more flexible and powerful environments for decision support.     

基于多属性决策的嵌入式操作系统识别技术研究2

针对嵌入式固件逆向解析过程中操作系统类型识别困难的问题,提出了一种基于多属性决策的嵌入式操作系统识别技术。对固件映像中反映出的嵌入式操作系统的多种特征进行综合分析并构建了相关的识别模型,利用向量夹角余弦计算与标准系统之间的相似度。阐述了识别的基本思想和具体实现流程。实验结果表明,该方法在某些特征缺失的情况下仍能得到较准确的识别结果。     

Improved voicing decision using glottal activity features for statistical parametric speech synthesis

A method to improve voicing decision using glottal activity features proposed for statistical parametric speech synthesis. In existing methods, voicing decision relies mostly on fundamental frequency $\mathrm{F}0$, which may result in errors when the prediction is inaccurate. Even though $\mathrm{F}0$ is a glottal activity feature, other features that characterize this activity may help in improving the voicing decision. The glottal activity features used in this work are the strength of excitation (SoE), normalized autocorrelation peak strength (NAPS), and higher-order statistics (HOS). These features obtained from approximated source signals like zero-frequency filtered signal and integrated linear prediction residual. To improve voicing decision and to avoid heuristic threshold for classification, glottal activity features are trained using different statistical learning methods such as the k-nearest neighbor, support vector machine (SVM), and deep belief network. The voicing decision works best with SVM classifier, and its effectiveness is tested using the statistical parametric speech synthesis. The glottal activity features SoE, NAPS, and HOS modeled along with $\mathrm{F}0$ and Mel-cepstral coefficients in Hidden Markov model and deep neural network to get the voicing decision. The objective and subjective evaluations demonstrate that the proposed method improves the naturalness of synthetic speech.     

A collaborative platform for cognitive decision making in the Knowledge Society

Two key aspects of the Knowledge Society are the interconnection between the actors involved in the decision making processes and the importance of the human factor, particularly the citizen’s continuous learning and education. This paper presents a new module devoted to knowledge extraction and diffusion that has been incorporated into a previously developed decision making tool concerning the Internet and related with the multicriteria selection of a discrete number of alternatives (PRIOR-Web). Quantitative and qualitative procedures using data and text mining methods have been employed in the extraction of knowledge. Graphical visualisation tools have been incorporated in the diffusion stage of the methodological approach suggested when dealing with decision making in the Knowledge Society. The resulting collaborative platform is being used as the methodological support for the cognitive democracy known as e-cognocracy.     

A decision support system for Evidence Based Medicine

We present a decision support system to let medical doctors analyze important clinical data, like patients medical history, diagnosis, or therapy, in order to detect common patterns of knowledge useful in the diagnosis process. The underlying approach mainly exploits case-based reasoning (CBR), which is useful to extract knowledge from previously experienced cases. In particular, we used sequence data mining to detect common patterns in patients histories and to highlight the effects of medical practices, based on evidence.We also exploited data warehousing techniques, such OLAP queries to let medical doctor analyze diagnosis along several measures, and recent visual data integration approaches and tools to effectively support the complex task of integrating and reconciling data from different medical data sources. In addition, due to massive presence of textual information within the clinical records of many hospitals, text mining techniques have been devised. In particular, we performed lexical analysis of free text in order to extract discriminatory terms and to derive encoded information. Finally, the system provides user friendly mechanisms to manage the protection of confidential medical data.System validation has been performed, mainly focusing on usability issues, by running experiments based on a large database from a primary public hospital.