时态逻辑断言推理及其在SIDSS中的应用

介绍了断言推理和时态逻辑断言的基本概念，并以工资智能决策支持系统(SIDSS)为例，讨论时态逻辑断言规则的设计、形式化描述与Java代码实现。     

湖北省突发事件预警应急智能决策支持系统

介绍了突发事件预警应急智能决策支持系统研究的必要性,阐述了湖北省突发事件预警应急智能决策支持系统的网络结构、系统的基本功能和系统的结构设计思想。重点讨论了该系统的关键技术——主推理机的设计。为解决案例推理和规则推理方面的不足,提出了一种新的FCR算法,它将案例推理、规则推理和模糊推理有机结合起来,并用于实际的智能决策支持系统。     

灰云逻辑及其智能决策支持系统

针对经典的数理逻辑不能满足智能决策决策支持系统对逻辑柔性化的要求,在分析各种经典和非经典逻辑的基础上,指出了智能决策决策支持系统的逻辑柔性化的发展趋势,并给出了一种基于灰云模型的具体的柔性逻辑——灰云逻辑。首先分析了智能决策支持系统对逻辑柔性化的需求,然后给出了灰云逻辑及基于灰云逻辑的智能决策支持系统,给出了灰云逻辑的具体表示形式,并给出了基于灰云逻辑的具体的知识推理方法。最后,给出了基于灰云逻辑的智能决策支持系统框架和原理。其研究特点在于明确了智能决策决策支持系统的逻辑柔性化的发展方向,并给出了一种表示信息不完全性和随机性的具体的柔性逻辑推理方法。     

Fuzzy branching temporal logic

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.     

面向对象的智能决策支持系统

研究如何应用面向对象技术进行智能决策支持系统的开发。首先提出面向对象智能决策支持系统的总体结构,然后介绍运用面向对象技术开发智能决策支持系统的系统分析和设计方法,最后分析了面向对象智能决策支持系统的特点,说明运用面向对象技术开发决策支持系统是有效的。     

Modeling and reasoning techniques in geologic interpretation

A thorough investigation is reported on the qualitative modeling of geologic systems, focusing on the reconstruction of three-dimensional (3-D) profiles from image data by means of spatial and temporal reasoning techniques. A conceptual model of the relevant knowledge is proposed for both the domain elements and the inference processes. At the former level the authors describe the objects in terms of geometric primitives and relations among them; at the inference level, reconstruction is identified as a synthesis task, in which a 3-D model of underground bodies results from assembling simpler components. The process is incremental and nomnonotonic, according to a basic assemble-validate-and-debug cycle, underlying both low-level and high-level steps. A formal (logical) model of the latter is proposed and worked out in detail. Concepts from topology and graph theory provide effective tools to define representations and algorithms, and allow one to address the intertwining of spatial and temporal knowledge. Some relevant reasoning steps are also regarded as constraint satisfaction problems. The authors analyze the constraints, show that the related tasks can be solved with algorithms of polynomial complexity, and provide the appropriate procedures. The practical feasibility of the model has been tested, and results of the applications to realistic input data are discussed. The authors also discuss solutions for embedding the modules into a man-machine interface for the intelligent support to the interpretation of data     

施工方案智能决策支持系统的面向对象模型

本文应用面向对象技术分析建筑施工组织设计，建立了包括施工项目子模型和施工活动子模型的施工领域模型，并在此基础上建立了施工方案智能决策支持系统模型。同时以钢筋砼建筑的施工方案决策为例，提出了知识库中知识的面向对象表示与增长机制以及决策过程的实现方法。     

Intelligent information and diagnostic system for examining blood vessels

An information and diagnostic system (IDS) for examining the state of large elastic human blood vessels based on the pulse wave shape is proposed. This intelligent system equipped with an expert system as a decision making tool can process biomedical signals arriving from pulse wave sensors and can carry out multivariant analysis of the calculated indicators and characteristics. An inference engine for estimating the membership of the vessel states in a class of diseases is proposed. This engine is implemented based on fuzzy classification principles. The decision making procedure is simulated and its efficiency is estimated.     

Developing and assessing an intelligent forex rolling forecasting and trading decision support system for online e‐service

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back‐propagation neural network (BPNN)‐based forex forecasting subsystem and Web‐based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e‐service for forex traders to make useful trading decisions in the forex market. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 475–499, 2007.     

Pluralistic multi-agent decision support system: a framework and an empirical test

Recent research in decision support systems (DSSs) has focused on building active cooperative intelligent systems. Research in agent-based decision support is a promising stream in this direction. This paper proposes a framework for a pluralistic multi-agent decision support system (MADSS). The distinguishing feature of the proposed approach is its organization around human decision making process. The framework builds upon the decision support pyramid with agents organized into groups according to the phases of the problem solving model. We outline the design principles and develop architecture for MADSS. The framework is illustrated through an investment MADSS prototype. The results of the empirical test are presented.     

基于知识图谱的智能决策支持技术及应用研究

知识图谱是把复杂的领域知识通过数据挖掘、信息处理、知识计量和图形绘制而显示出来,解释知识领域的动态发展规律。知识图谱把所有不同种类的信息(heterogeneous information)连接在一起得到一个关系网络并从"关系"的角度去分析问题。知识图谱目前被广泛应用于智能搜索、智能问答等领域。提出了一种基于知识图谱的智能决策支持框架,用于解决传统决策支持系统存在的问题。通过大数据、知识图谱等海量知识分析和模型构建技术,结合决策支持系统,增强对问题的分解与处理、形成具有关系型网络的知识系统。最后结合电信领域中的经典决策案例,搭建基于知识图谱的欺诈电话智能决策支撑平台。和传统的决策支持系统比较,该研究方法的优点在于结合大数据处理方法提升了知识建模的算力和决策支持的效率,使实时处理大规模信息数据成为现实;基于知识图谱的关系型网络,提升了决策模型的准确性和关联相关性。     

A general knowledge mediation infrastructure for multi-agent systems

Intelligent decision making needs to be equipped with broader knowledge in order to enhance the decision quality. Knowledge for decision making can be categorized as domain specific and general. Applying domain knowledge in intelligent systems is not new, but applying general knowledge to support business decision making is a possible way to obtain an edge over competitors. For this reason, the paper focuses primarily on designing a general knowledge mediation infrastructure (GKMI) which supports the use of general knowledge from multiple heterogeneous sources, and provides an unified access point for typical multi-agent systems (MAS) to access that knowledge. The finite state automaton (FSA) is used to model and analyze the commonsense inference ability of GKMI. By carrying out two use cases of GKMI for MAS development and operation the effectiveness of this infrastructure is examined.     

Handling of Alternatives and Events in Temporal Databases

Planning for the future is an important activity both at the individual and organizational levels. Planning consists of defining alternative actions to handle various events in the future. The alternatives arise becau]se of different possible outcomes of events. A plan consists of a sequence of actions to be carried out for each possible outcome. In the context of database modeling, the actions are operations on a database. A database management system should enable its users to define events and alternatives, and also allow them to interact with the database under different alternatives (possibly to evaluate different plans). The existing temporal data models treat the future analogous to the past or present; they provide for one future path (in the sense that facts valid at some future time can be stored), but do not provide support for alternatives in the future. In this paper, we present a model for incorporating events and alternatives by extending the temporal data model to support branching time. The extended model permits definitions of events, their interdependencies and associated actions. The events that affect an object are modeled by a tree, permitting an object to have different states at the same valid time but under different alternatives. The branching time paradigm is obtained by superimposing a linear valid time on the event tree. We extend the temporal relational algebra and the Temporal SQL2 to support a branching time data model. The paper also briefly deals with the uncertainties associated with future planning as well as probabilities of possible event outcomes. Finally, we sketch an implementation strategy for the branching time data model.     

基于判别图模型的激光点云数据关联决策

提出一种激光点云数据关联决策算法. 基于判别图模型, 提取并智能管理激光点云的多重形状特征, 通过最 大伪似然学习优化局部特征和配对特征的权重; 应用最大和概率推理实现对图模型隐节点状态的估计, 进而将激光点关联映射为最大后验概率的配置回溯问题; 实验结果验证了所提出算法比传统算法具有更好的性能.

     

Modeling and Inference of Extended Interval Temporal Logic for Nondeterministic Intervals

Extended interval temporal logic (EITL), an extension of the traditional point-interval temporal logic (PITL), is proposed. In contrast to PITL that represents the dynamic aspects of deterministic intervals, EITL can model and reason about the temporal relations among nondeterministic intervals in discrete-event systems, in which the duration of an event is indeterminate and only the lower bound and upper bound of the ending time can be predicted in advance. Time Petri nets (TPNs) are used for modeling EITL, for they give a straightforward view of temporal relations between the extended intervals and also provide a number of theoretical and practical analysis methods. An inference engine based on the TPN modeling complemented with algebraic inequalities is proposed to construct an analytical representation of the EITL relations and solve qualitative temporal reasoning problems. Linear inference mechanism based on TPN reduction rules is used to infer new temporal relations and handle quantitative temporal reasoning problems with linear time complexity, as our example shows.     

Context-prediction performance by a dynamic Bayesian network: Emphasis on location prediction in ubiquitous decision support environment

Ubiquitous decision support systems require more intelligent mechanism in which more timely and accurate decision support is available. However, conventional context-aware systems, which have been popular in the ubiquitous decision support systems field, cannot provide such agile and proactive decision support. To fill this research void, this paper proposes a new concept of context prediction mechanism by which the ubiquitous decision support devices are able to predict users’ future contexts in advance, and provide more timely and proactive decision support that users would be satisfied much more. Especially, location prediction is useful because ubiquitous decision support systems could dynamically adapt their decision support contents for a user based on a user’s future location. In this sense, as an alternative for the inference engine mechanism to be used in the ubiquitous decision support systems capable of context-prediction, we propose an inductive approach to recognizing a user’s location by learning a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power in the location prediction. Besides, we found that the dynamic Bayesian network model has a great potential for the future types of ubiquitous decision support systems.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

This study addresses flexible job-shop scheduling problem (FJSP) with fuzzy processing time. An improved artificial bee colony (IABC) algorithm is proposed for FJSP cases defined in existing literature and realistic instances in remanufacturing where the uncertainty of the processing time is modeled as fuzzy processing time. The objectives are to minimize the maximum fuzzy completion time and the maximum fuzzy machine workload, respectively. The goal is to make the scheduling algorithm as part of expert and intelligent scheduling system for remanufacturing decision support. A simple and effective heuristic rule is developed to initialize population. Extensive computational experiments are carried out using five benchmark cases and eight realistic instances in remanufacturing. The proposed heuristic rule is evaluated using five benchmark cases for minimizing the maximum fuzzy completion time and the maximum fuzzy machine workload objectives, respectively. IABC algorithm is compared to six meta-heuristics for maximum fuzzy completion time criterion. For maximum fuzzy machine workload, IABC algorithm is compared to six heuristics. The results and comparisons show that IABC algorithm can solve FJSP with fuzzy processing time effectively, both benchmark cases and real-life remanufacturing instances. For practical remanufacturing problem, the schedules by IABC algorithm can satisfy the requirement in real-life shop floor. The IABC algorithm can be as part of expert and intelligent scheduling system to supply decision support for remanufacturing scheduling and management.