A cost-reducing question-selection algorithm for propositional knowledge-based systems

In many applications of knowledge-based systems, initially given data are often not sufficient to reach a conclusion and more data are needed. A question-selection algorithm is to identify missing information and select proper questions to ask. We present a question-selection algorithm for propositional knowledge-based systems, which aims at asking more relevant and less expensive questions. Comparing to those algorithms currently used in many expert systems, the new algorithm is capable of reaching a conclusion more economically in our computational experiments. AMS subject classification 03B05, 68T20, 68T15     

基于多属性判决的网络选择算法

异构网络中,针对如何根据不同业务的特性选择合适的判决参数并利用这些参数来选择最适合的网络以及通过网络负载均衡来减少切换次数和切换失败可能性等问题,提出了一种基于多属性判决的网络选择算法——TAU-MADM算法.考虑到网络客观属性和用户偏好,进行两次判决:第一次判决采用TOPSIS算法,仅考虑网络客观属性,当备选网络方案相当接近时进行第二次判决;第二次判决采用AHP算法计算权重,并采用ANP网络分析法消除属性之间的依赖性,之后构造效用函数,根据每个网络的效用函数值进行网络选择.仿真结果表明,该算法考虑了网络负载均衡,有效地降低了平均切换率和平均切换阻塞卒.     

Extremist vs. centrist decision behavior: quasi-convex utility functions for interactive multi-objective linear programming problems

This paper presents the fundamental theory and algorithms for identifying the most preferred alternative for a decision maker (DM) having a non-centrist (or extremist) preferential behavior. The DM is requested to respond to a set of questions in the form of paired comparison of alternatives. The approach is different than other methods that consider the centrist preferential behavior.In this paper, an interactive approach is presented to solve the multiple objective linear programming (MOLP) problem. The DM's underlying preferential function is represented by a quasi-convex value (utility) function, which is to be maximized. The method presented in this paper solves MOLP problems with quasi-convex value (utility) functions by using paired comparison of alternatives in the objective space. From the mathematical point of view, maximizing a quasi-convex (or a convex) function over a convex set is considered a difficult problem to solve, while solutions for quasi-concave (or concave) functions are currently available. We prove that our proposed approach converges to the most preferred alternative.We demonstrate that the most preferred alternative is an extreme point of the MOLP problem, and we develop an interactive method that guarantees obtaining the global most preferred alternative for the MOLP problem. This method requires only a finite number of pivoting operations using a simplex-based method, and it asks only a limited number of paired comparison questions of alternatives in the objective space. We develop a branch and bound algorithm that extends a tree of solutions at each iteration until the MOLP problem is solved. At each iteration, the decision maker has to identify the most preferred alternatives from a given subset of efficient alternatives that are adjacent extreme points to the current basis. Through the branch and bound algorithm, without asking many questions from the decision maker, all branches of the tree are implicitly enumerated until the most preferred alternative is obtained. An example is provided to show the details of the algorithm. Some computational experiments are also presented.Scope and purposeThis paper presents the fundamental theory, algorithm, and examples for identifying the most preferred alternative (solution) for a decision maker (DM) having a non-centrist (or extremist) preferential behavior for Multiple Objective Linear Programming (MOLP) problems. The DM is requested to respond to a set of questions in the form of paired comparison of alternatives.Although widely applied, Linear Programming is limited to a single objective function. In many real world situations, DMs are faced with multiple objective problems in that several competing and conflicting objectives have to be considered. For these problems, there exist many alternatives that are feasible and acceptable. However, the DM is interested in finding “the most preferred alternative”. In the past three decades, many methods have been developed for solving MOLP problems.One class of these methods is called “interactive”, in which the DM responds to a set of questions interactively so that his/her most preferred alternative can be obtained. In most of these methods, the value (utility) function (that presents the DM's preference) is assumed to be linear or additive, concave, pseudo-concave, or quasi-concave. However, for MOLP problems, there has not been any effort to recognize and solve the quasi-convex utility functions, which are among the most difficult class of problems to solve. The quasi-convex class of utility functions represents an extremist preferential behavior, while the other aforementioned methods (such as quasi-concave) represent a conservative behavioral preference. It is shown that the method converges to the optimal (the most preferred) alternative. The approach is computationally feasible for moderately sized problems.     

Analysis of heuristic validity,efficiency and applicability of the profile distance method for implementation in decision support systems

This article seeks to enhance acceptance of the profile distance method (PDM) in decision support systems. The PDM is a multiple attributive-based decision making as well as a multiple method approach to support complex decision making and uses a heuristic to avoid computationally complex global optimization. We elaborate on the usability of the method and question the heuristic used. We present a bi-section algorithm, which efficiently supports the discovery of transition profiles needed in a user-friendly and practical application of the method. Additionally, we provide empirical evidence showing that the proposed heuristic is efficient and delivers results within 5% of the global optimizer for a wide range of datasets.     

基于网络的中文问答系统的研究

文章设计并实现了一个基于网络的中文问答系统。该系统只利用网络搜索引擎返回结果中的摘要部分作为答案抽取的资源,从而节省了下载、分析网络源文本的时间,提出了一种针对该系统的信息抽取算法,并采用一种基于语句相似度计算的答案抽取算法并且进行了改进。实验结果表明该系统对人名及时间类型的问题效果显著。对测试问题集的MRR值达到0.47。     

Constraint-based optimization and utility elicitation using the minimax decision criterion

In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which multiple users have distinct preferences. However, making suitable decisions requires that the preferences of a specific user for different configurations be articulated or elicited, something generally acknowledged to be onerous. We address two problems associated with preference elicitation: computing a best feasible solution when the user's utilities are imprecisely specified; and developing useful elicitation procedures that reduce utility uncertainty, with minimal user interaction, to a point where (approximately) optimal decisions can be made. Our main contributions are threefold. First, we propose the use of minimax regret as a suitable decision criterion for decision making in the presence of such utility function uncertainty. Second, we devise several different procedures, all relying on mixed integer linear programs, that can be used to compute minimax regret and regret-optimizing solutions effectively. In particular, our methods exploit generalized additive structure in a user's utility function to ensure tractable computation. Third, we propose various elicitation methods that can be used to refine utility uncertainty in such a way as to quickly (i.e., with as few questions as possible) reduce minimax regret. Empirical study suggests that several of these methods are quite successful in minimizing the number of user queries, while remaining computationally practical so as to admit real-time user interaction.     

A stochastic multicriteria acceptability analysis–evidential reasoning method for uncertain multiattribute decision‐making problems

Evidential reasoning (ER) is an effective approach for assessing alternatives with uncertain attribute values in the context of decision making. For the ER approach to be able to handle variations in the weights of uncertain attributes in an appropriate manner, this paper proposes a method to solve problems of uncertain multiattribute decision making that involve both uncertain attribute values and uncertain attribute weights, which this method does by combining the ER approach and stochastic multicriteria acceptability analysis‐2 (SMAA‐2). First, the uncertainty in attribute values is described by using a belief decision matrix as in the ER approach. The analytical ER algorithm is then used to create the utility function in the SMAA‐2 model, and that function is used to calculate the probability of different sorting positions of the decision units under weight‐related restrictions. Finally, the results of ranking are obtained by combining the sorting weights. An example is provided to verify the effectiveness of the proposed method.     

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.     

Display panel design of a general utility helicopter by applying quantitative and qualitative approaches

The flight deck is the ultimate human–machine interface application. It uses human senses of touch and sight in a safety‐critical situation. Display systems, present on a flight deck, are complex environments causing excessive mental workload, and the consequences of errors can be catastrophic. To prevent any human error, a user‐friendly display panel must be designed. In this research the best natural dialog between the crew and interface is considered while reflecting user perspective to design by applying quantitative and qualitative approaches; in this way, proper positions of analogue indicators on the front of the display panel are determined. Multi‐Criteria Decision Making algorithms are employed as a quantitative approach. To test whether the decision‐making preferences are consistent with a quasi‐concave, a quasi‐convex, and a linear utility function, a combined algorithm is applied. Due to the form of utility function, an algorithm finding the most preferred alternative of decision making is used. The aim of this approach is to find an order in which to locate each indicator to suitable classes. Card‐Sorting methodology is used for the qualitative research of the same problem. Results of two different approaches are compared. © 2009 Wiley Periodicals, Inc.     

基于ASP试题库系统的设计

论述了《计算机操作系统》试题库系统的总体设计结构,系统采用B/S结构,应用ASP技术和采用Access数据库来构成试题库系统,使用ODBC技术连接后台数据库,并且给出了本系统的需求分析、系统结构图、系统各模块的功能,在数据库设计中用数据流图来表达数据和处理的关系,说明了每个数据库表的详细信息。系统在抽题算法上,采用了随机抽取与设置难度系数相结合的方式,这样可以使每个学生抽到的试题与其他学生不完全一样,并且试卷中各种难度试题数量的分布更加合理化、科学化,同时给出了详细的抽取算法。     

A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information

Manufacturing technology selection is traditionally a human-driven approach where the trade-off of alternative manufacturing investments is steered by a group of experts. The problem is a semi-structured and subjective-based decision practice influenced by the experience and intuitive feeling of the decision-makers involved. This paper presents a distinct experience-based decision support system that uses factual information of historical decisions to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements. A fuzzy-decision-tree algorithm is applied to provide a more objective approach given the evidence of previous manufacturing technology implementation cases. The model uses the information relationship of key technology decision variables, project requirements of an implemented technology case and the success outcome of a project to support decision problems. An empirical study was conducted at an aircraft manufacturer to support their technology decision for a typical medium complexity assembly investment project. The experimental analysis demonstrated encouraging results and practical viability of the approach.     

基于VF6.0的学校考试自动组卷系统

在对学校考题进行全面分析的基础上，提出了题库过滤、随机抽题、人工调整试题难度相结合的成卷控制算法，介绍了自动组卷系统的设计思想、系统结构及各模块的设计方法，阐明了该系统从录题、抽题到组卷的全过程，并对试卷输出及系统维护做了简要说明。     

Quantifying information security risks using expert judgment elicitation

In the information security business, 30 years of practical and theoretical research has resulted in a fairly sophisticated appreciation for how to judge the qualitative level of risk faced by an enterprise. Based upon that understanding, there is a practical level of protection that a competent security manager can architect for a given enterprise. It would, of course, be better to use a quantitative approach to risk management, but, unfortunately, sufficient quantitative data that has been scientifically collected and analyzed does not exist. There have been many attempts to develop quantitative data using traditional quantitative methods, such as experiments, surveys, and observations, but there are significant weaknesses apparent in each approach. The research described in this paper was constructed to explore the utility of applying the well-established method of expert judgment elicitation to the field of information security. The instrument for eliciting the expert judgments was developed by two information security specialists and two expert judgment analysis specialists. The resultant instrument was validated using a small set of information security experts. The final instrument was used to elicit answers to both the calibration and judgment questions through structured interviews. The data was compiled and analyzed by a specialist in expert judgment analysis. This research illustrates the development of prior distributions for the parameters of models for cyber attacks and uses expert judgment results to develop the distributions.     

CDN中基于服务效用的请求重定向策略

改进了一种评测内容分发网络服务效用的量化标准,以体现内容分发系统的感知得益,表达各副本服务器的服务效能,并在效用评测的基础上提出了一种请求重定向策略。通过仿真模拟测试,表明该请求重定向策略可以提高内容分发网络的服务性能。     

An interactive computer-based interface to support the discovery of individuals’ mental representations and preferences in decisions problems: An application to travel behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints.     

HUC-Prune: an efficient candidate pruning technique to mine high utility patterns

Traditional frequent pattern mining methods consider an equal profit/weight for all items and only binary occurrences (0/1) of the items in transactions. High utility pattern mining becomes a very important research issue in data mining by considering the non-binary frequency values of items in transactions and different profit values for each item. However, most of the existing high utility pattern mining algorithms suffer in the level-wise candidate generation-and-test problem and generate too many candidate patterns. Moreover, they need several database scans which are directly dependent on the maximum candidate length. In this paper, we present a novel tree-based candidate pruning technique, called HUC-Prune (High Utility Candidates Prune), to solve these problems. Our technique uses a novel tree structure, called HUC-tree (High Utility Candidates tree), to capture important utility information of the candidate patterns. HUC-Prune avoids the level-wise candidate generation process by adopting a pattern growth approach. In contrast to the existing algorithms, its number of database scans is completely independent of the maximum candidate length. Extensive experimental results show that our algorithm is very efficient for high utility pattern mining and it outperforms the existing algorithms.     

A novel two‐stage model for cloud service trustworthiness evaluation

In this paper, we address the cloud service trustworthiness evaluation problem, which in essence is a multi‐attribute decision‐making problem, by proposing a novel evaluation model based on the fuzzy gap measurement and the evidential reasoning approach. There are many sources of uncertainties in the process of cloud service trustworthiness evaluation. In addition to the intrinsic uncertainties, cloud service providers face the problem of discrepant evaluation information given by different users from different perspectives. To address these problems, we develop a novel fuzzy gap evaluation approach to assess cloud service trustworthiness and to provide evaluation values from different perspectives. From the evaluation values, the perception–importance, delivery–importance, and perception–delivery gaps are generated. These three gaps reflect the discrepancy evaluation of cloud service trustworthiness in terms of perception utility, delivery utility, and importance utility, respectively. Finally, the gap measurement of each perspective is represented by a belief structure and aggregated using the evidential reasoning approach to generate final evaluation results for informative and robust decision making. From this hybrid two‐stage evaluation process, cloud service providers can get improvement suggestions from intermediate information derived from the gap measurement, which is the main advantage of this evaluation process.     

A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.     

面向课程的中文FAQ自动问答系统模型

面向课程的中文FAQ自动问答系统作为网络教学平台中的子系统,可以实现课程自动答疑,具有较强的实用性,该类问答系统的FAQ库大都人工构建,费时费力,针对此种状况,本文提出一种新的问答系统模型,从"百度知道"自动获取不同课程FAQ库的问答对,并根据提出的融合语义词典和句法依存关系的句子相似度算法,实现学生问句与根据问句关键字向量得到的FAQ库检索子集中的问句之间的相似度计算,自动应答,其他情况采用人工解答,并更新FAQ库。实验结果表明,本系统模型,具备根据不同课程自动构建高质量FAQ库的能力,具备更高的应答准确率,具备不断增强答疑效果的能力,具有广阔的应用前景。