专家系统中基于Jess的推理研究

介绍了专家系统开发平台Jess,并把Jess运用到民航机务维修差错预警专家系统中进行研究.该系统中维修差错以规则形式表示,推理机使用基于规则的不确定性推理方法进行推理,使系统达到有效预测和控制可能发生的维修差错,从而保证飞行安全.     

基于规则的专家系统推理框架JESS应用研究

首先介绍专家系统推理框架Jess的知识表示、基本组成、工作原理和开发环境,然后以人工智能经典游戏难题——野人传教士过河问题为例,用Jess实现了问题描述、知识抽取、结构定义、应用程序实现,进一步探索利用Jess的知识库和推理机分离特点,逐步扩充和更新知识库,不断增强和完善应用程序功能,最终实现一个复杂智能问题求解的新方法。     

基于Java平台的专家系统

介绍了开发专家系统的相关技术,探讨了如何利用Jess灵活的推理机制和功能强大的Java语言相结合来开发专家系统。     

如何用Jess开发专家系统

在当今的人工领域当中，出现了许多以Java为核心技术的专家系统开发工具，Jess(Java expert system shell)就是一种基于Java的专家系统外壳，本文简要介绍了Jess的内容，并给出了用它开发专家系统的一些基本的应用方法。     

采用Jess引擎的机械设备故障诊断专家系统设计

介绍一种在J2EE环境下构建机械设备故障诊断专家系统的方案,使用Jess引擎作为核心推理模块,通过WebService和OPC技术等多途径获取事实,采用数据库管理事实和规则,实现基于Web的机械设备故障诊断专家系统,使机械设备故障诊断系统融合领域专家的经验、智慧和方法,这对于保证设备安全运行具有重大意义。     

基于JADE和Jess的智能agent系统开发

多agent技术在复杂建模领域具有优势,是当前研究的一个热点。但对于普通研究者来说,从底层开发多agent系统具有复杂性。因此,该文分析了多agent系统的分析和设计过程,讨论了以JADE为开发工具的多agent系统开发步骤,并设计了基于Jess的知识推理流程,增强了agent智能性。对基于JADE和Jess的智能agent系统的开发过程进行了研究。     

Prolog与Jess推理算法比较

随着语义网和专家系统的发展,产生了许多基于逻辑表示的推理机,比如Jess、Prolog等.对于推理机来说,推理的能力和效率至关重要,Jess采用rete算法,Prolog采用深度优先算法,针对这两种推理算法进行比较,以便能够对推理机有更深入的了解.     

基于Jess和Java混合编程的楼宇温度控制系统仿真实现

要求控制一幢大楼各个楼层温度,使其都保持在温度设定值附近的一个小的偏移量范围之内。针对此非线性温度控制系统,分析比较其它高级语言及PLC在实现此类控制方面的算法特点,最终采用上位机控制,选用Jess语言和Java语言混合编程,设计开发了一个楼宇温度控制仿真系统。选用Jess语言编写控制规则,控制空调机在多个状态之间的切换,控制通风口在打开和关闭两状态之间的切换;使用Java语言扩展Jess的功能函数,创建仿真器对象,搭建监控GUI界面,为诸多被控对象开辟新线程,提高控制效率等。针对不同环境温度和系统扩容的仿真实验测试结果表明,该仿真系统是正确的和高效的,为实际系统的正确搭建奠定了坚实的基础。     

基于语义Web的受控自然语言系统推理模型

虽然相关研究组织提供了语义Web的一些简化工具,但是对不具备相关背景知识的领域专家来说,语义Web的可用性较低。提出了基于语义Web的受控自然语言系统推理模型,以解决这个问题。首先给出受控自然语言系统推理模型框架;然后分析受控自然语言的语言处理部分,提出基于WordNct的受控自然语言系统的本体词库模型和基于本体词库的受控自然语言解释器,把受控自然语言转换成中间表达语言篇章表述结构;最后通过推理部分把篇章表述结构转换成语义Web的本体和规则,通过模板工具映射成Jess的事实和规则,根据预定义的语义Web的公理和定理对受控自然语言进行推理。试验证明此模型大大提高了知识表示建模的效率,也基本满足简单推理任务,具有实用价值。     

基于规则引擎的计算机故障智能诊断系统的研究与实现

针对现有计算机故障诊断系统的不足,利用Jess规则引擎开发核心推理模块,遵循JSR 94标准,采用Java语言构建整体框架,并使用Web页面作为人机交互的平台,实现了具有可扩展性的计算机故障智能诊断系统。     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

Using JessTab to integrate Protege and Jess

Integration with external systems, such as problem solvers, is becoming increasingly important for ontology development and knowledge-modeling tools. The author's JessTab extension lets you write Jess programs that manage Protege ontologies and knowledge bases. Protege is a popular, modular ontology development and knowledge acquisition tool.     

基于三角模糊AHP的项目融资决策模型

通过对项目融资模式进行系统分析,构建了一个全新的基础设施项目融资模式决策模型。运用层次分解法将复杂项目融资模式决策问题进行分解简化,采用德尔菲法专家打分对备选模式对应的分解指标进行评价。针对专家意见具有模糊性和不确定性的特点,运用三角模糊语言变量,将专家意见转换成三角模糊数并构建模糊决策判断矩阵,再结合层次分析法(AHP)对判断矩阵进行模糊变换和向量计算,得到备选模式综合权重并据以做出决策。最后进行实例验证,为城市基础设施项目融资提供决策参考。     

基于Jess推理的网络消费者非理性购买预测

随着电子商务的高速发展,网络购物越来越经济便捷,相比于传统的网下购物,更多的消费者选择网购,这就使得非理性购买行为大量涌现,研究网络消费者非理性行为势在必行。然而,学者们大多关注传统购物环境下的非理性购买行为,并且研究内容比较琐碎,缺乏系统的框架。考虑到很多消费者选择通过在线评论表达对购买行为的情感和观点,因此首先利用情感计算和文本挖掘技术,在充分挖掘语义资源的基础上,借助模糊数学理论构建模糊语义模型,同时,以内、外部诱导因素为前件,以消费者非理性购买程度作后件,建立了模糊推理模型;然后,针对消费者非理性购买行为和过程,通过protégé建立本体,梳理各个因素之间的联系,构建知识库;最后,利用Jess构建模糊推理事实库与规则库,通过Jess推理机获取消费者非理性购买程度。     

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.     

Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in the Production Rule Representation language (PRR). Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge enables an early modeling of user requirements in a data warehouse project. A prototype has been developed based on the Java Expert System Shell (Jess).     

Integration of human knowledge and machine knowledge by using fuzzy post adjustment: its performance in stock market timing prediction

Abstract: This paper proposes a fuzzy post adjustment (FPA) mechanism so that human knowledge and machine knowledge can be integrated more synergistically to improve the performance of expert systems. Machine knowledge means knowledge algorithmically derived from past instances. Human knowledge implies (1) expert knowledge judging the trends of external factors and (2) user knowledge representing users'personal views about information given by both expert knowledge and machine knowledge. We consider an expert system that uses the FPA mechanism to incorporate the effect of external factors effectively into its inference process. The goal of this expert system is stock market timing prediction, which is divided into four kinds: bull, edged-up, edged-down and bear. Empirical tests showed that the proposed FPA mechanism can improve the performance of an expert system significantly, even in a turbulent decision-making environment.