大数据量推理专家系统外壳的研究与设计

CLIPS是现今主流的专家系统开发工具,但它不能直接访问外部数据库,而传统数据库又缺乏知识。本文提出将CLIPS与数据库系统Postgres结合,使用知识开发技术来降低存储在CLIPS主存储器内数据量。在此结构中,数据存储于Postgres,两系统的接口负责知识归纳,继而将知识传送到CLIPS的工作存储器进行推理。增加了CLIPS的大数据量推理能力。     

实时数据及时态知识的表示

在实时专家系统中引入时间概念后，涉及时间的知识的表示成为值得注意的问题，本文在分析一些典型事例的基础上，着重探讨以下３个问题，如时间数据的有效表示；知识中时间特征的表示；推理中知识的时间一致性检验等.     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

不确定时态信息表示的统一模型

时态信息表示和推理是人工智能研究中的一个重要课题,现有的模型大多只能表示确定时态信息,然而现实生活中很多事件的发生结束等时态信息都是不确定的。故提出了一个表示不确定时态信息的统一模型,可用于描述各种具有确定或不确定时态信息的事件。该模型首先定义各类时态对象(如时间点、时间区间)以及它们之间的关系,并给出时态对象间的传递关系表,利用该表能进行时态一致性约束满足问题的求解。最后,给出了两个不确定时态推理的例子,表明了该模型的实际应用意义。     

Lattice structure of temporal interval relations

Due to increasing interest in representation of temporal knowledge, automation of temporal reasoning, and analysis of distributed systems, literally dozens of temporal models have been proposed and explored during the last decade. Interval-based temporal models are especially appealing when reasoning about events with temporal extent but pose special problems when deducing possible relationships among events. The paper delves deeply into the structure of the set of atomic relations in a class of temporal interval models assumed to satisfy density and homogeneity properties. An order structure is imposed on the atomic relations of a given model allowing the characterization of the compositions of atomic relations (or even lattice intervals) as lattice intervals. By allowing the utilization of lattice intervals rather than individual relations, this apparently abstract result explicitly leads to a concrete approach which speeds up constraint propagation algorithms.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

基于模糊推理的物流车辆故障诊断专家系统

物流车辆故障诊断专家系统可以对物流车辆的故障进行诊断和排除. 为了提高该系统快速、准确诊断的能力, 在分析物流车辆的故障模式和故障机理的基础上, 建立故障树, 采用改进的CLIPS可以进行正向、反向两种模糊推理机制, 同时建立知识库管理系统对模糊规则和事实进行管理. 研究结果表明: 改进的CLIPS与VC++的结合, 使物流车辆故障诊断专家系统拥有模糊诊断故障的能力, 提高了物流车辆故障诊断的智能化水平.     

基于CLIPS的油水井动态分析模型研究与实现

针对当前油田油水井动态分析流程复杂、计算量大并依赖于经验丰富的专家等问题,提出一种基于CLIPS专家系统外壳的油水井动态分析模型。获取油水井动态分析经验知识并对其分类总结,研究知识的表示、存储、管理技术以及将知识编译成符合CLIPS语法形式的方法,设计数据-事实解释机制实现从油田生产数据库自动抽取推理事实,利用CLIPS内核进行推理并对结果进行解释。最后将油水井动态分析模型应用在油田实际项目中,证明了该模型的可行性和有效性。     

DISLOG: programming in logic with discontinuities

In this paper, we present an extension to PROLOG we call DISLOG which is designed to deal with relations between non-contiguous elements in a structure. This extension turns out to be well suited for syntactic analysis of natural and artificial languages. It is also well adapted to express traversal constraints in applications such as planning and expert systems and deductive systems involving, for example, temporal reasoning, DISLOG belongs to the constrained logic programming paradigm and turns out to be more declarative, transparent, and simple than PROLOG to deal with longdistance relations.     

From event calculus to the scheduling problem. Semantics of action and temporal reasoning in aircraft maintenance

In this work we are interested in the logical and semantical aspects of reasoning about actions in a scheduling process. We present an adaptation of the event calculus of Kowalski and Sergot to the problem of determining the temporal structure of the operations that must be performed during the realization of some complex objectives. Our application domain is aircraft maintenance. We try to reason about the actions which are performed during an overhaul in order to help to schedule them. The original model reasons about changes, i.e. events which initiate or terminate propositions. The first step of this work was to improve the initial model by adding a temporal relation between events and propositions because in our field we also have to reason about events which only inform us about some propositions without affecting them. The second step of this work is to build a set of specific rules which temporally interpret the semantics of the usual specifications of the actions to be considered. This interpretation aims to associate each action with two events and some temporal relations which are usable by the general model. Temporal reasoning uses pertinent knowledge about the specific universe (here, the aircraft that we consider and the actions which may be performed on it). We outline a generative methodology to formalize this relevant knowledge efficiently. This cognitive approach brings more informational economy in temporal reasoning because only the relevant information is considered The temporal reasoning model and the methodology have been exemplified and tested on a complex part of an aircraft. In the future, adapted tools based on this approach will be developed, in order to solve several problems of aircraft maintenance scheduling.     

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.     

可视化产生式系统的设计与实现

产生式系统是最流行的专家系统类型。目前常见的产生式系统,如OPS5和CLIPS,它们的应用和开发界面都是类似LISP的文本界面,可操作性差。文章基于面向对象的方法和技术,设计并实现了一个可视化的产生式系统,该系统采用Rete算法进行推理,提供了事实库、规则库和Rete网络的可视化的维护功能。实际运行表明,该系统效率高,可操作性强。     

Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system

The so-called "first generation" expert systems were rule-based and offered a successful framework for building applications systems for certain kinds of tasks. Spatial, temporal, and causal reasoning, knowledge abstractions, and structuring are among topics of research for "second generation" expert systems. It is proposed that one of the keys for such research is knowledge organization. Knowledge organization determines control structure design, explanation and evaluation capabilities for the resultant knowledge base, and has strong influence on system performance. We are exploring a framework for expert system design that focuses on knowledge organization, for a specific class of input data, namely, continuous, time-varying data (image sequences or other signal forms). Such data are rich in temporal relationships as well as temporal changes of spatial relations, and are thus a very appropriate testbed for studies involving spatio-temporal reasoning. In particular, the representation formalism specifies the semantics of the organization of knowledge classes along the relationships of generalization/specialization, decomposition/aggregation, temporal precedence, instantiation, and expectation-activated similarity. Á hypothesize-and-test control structure is driven by the class organizational principles, and includes several interacting dimensions of search (data-driven, model-driven, goal-driven temporal, and failure-driven search). The hypothesis ranking scheme is based on temporal cooperative computation, with hypothesis "fields of influence" being defined by the hypothesis' organizational relationships. This control structure has proven to be robust enough to handle a variety of interpretation tasks for continuous temporal data. A particular incarnation, the ALVEN system, for left ventricular performance assessment from X-ray image sequences, will be summarized in this paper.     

结合CLIPS和VC++实现规则推理的方法

专家系统开发通常有三种方式：高级程序语言,专家系统外壳,专家系统工具。该文介绍了专家系统工具CLIPS6和高级程序语言VC++6.0的各自特点,提出了将专家系统工具CLIPS6与VC＋＋6.0高级语言编程工具结合起来开发专家系统,实现规则推理。详细描述了CLIPS嵌入VC++的一般过程：如何把CLIPS6嵌入VC++,如何加入CLIPS用户自定义函数来传递和返回参数。并以摩托车智能设计为例,详细阐述了规则编辑,事实获取,实现规则解释,实现人机交互功能等,从而实现摩托车智能设计的规则推理。     

Consistency-motivated reason maintenance in hypothetical reasoning

We present a general framework for logic-based hypothetical reasoning, which introduces truth maintenance, as a collection of truth tables, to Theorist-like hypothetical reasoning systems. This framework uses full first-order logic, performs nonmonotonic reasoning by maintaining refutable theories, and incrementally identifies and records information (similar to the TMS-like caching mechanism) about inconsistencies and derived implicit knowledge, to improve overall search efficiency. The proposed framework retains the simplicity of hypothetical reasoning and adds the advantage of an incremental dynamic truth maintenance system. Experimental results provide evidence for efficiency advantages of the proposed framework.     

动态系统的模糊时序知识表达、推理及其应用

本文以工业动态系统的知识表达和问题求解为背景,提出了一个描述动态系统行为的模 糊时序模型、相应的时序匹配与数值匹配和推理方法.并将它们应用于建立催化裂化装置的 故障诊断专家系统.这一研究有利于沟通人工智能与控制理论的联系.     

蜜桔种植专家系统的设计与实现

介绍使用CLIPS工具开发蜜桔种植专家系统。该专家系统具有图片、影像等多媒体推理能力。核心组件包括知识库、规则表、推理机。推理机采用软件虚拟机的方式运行。该系统能够提高蜜桔的产量和减少病虫害的发生,对农业现代化具有积极意义。     

在ObjectARX中嵌入CLIPS的技术实现

探讨了运用专家系统工具CLIPS与ObjectARX混合编程开发工程制图专家系统的方法。利用ARX开发应用程序并通过调用CLIPS动态链接库,实现了如何在ARX应用程序中启动CLIPS以完成推理,并把结果返回给ARX应用程序的过程。     

Integrating multiple paradigms within the blackboard framework

The complexity and diversity of real world applications have forced researchers in the AI field to focus more on the integration of diverse knowledge representation and reasoning techniques for solving challenging, real world problems. Our development environment, BEST (Blackboard based Expert Systems Toolkit), is aimed to provide the ability to produce large scale, evolvable, heterogeneous intelligent systems. BEST incorporates the best of multiple programming paradigms in order to avoid restricting users to a single way of expressing either knowledge or data. It combines rule based programming, object oriented programming, logic programming, procedural programming and blackboard modelling in a single architecture for knowledge engineering, so that the user can tailor a style of programming to his application, using any or arbitrary combinations of methods to provide a complete solution. The deep integration of all these techniques yields a toolkit more effective even for a specific single application than any technique in isolation or collections of multiple techniques less fully integrated. Within the basic, knowledge based programming paradigm, BEST offers a multiparadigm language for representing complex knowledge, including incomplete and uncertain knowledge. Its problem solving facilities include truth maintenance, inheritance over arbitrary relations, temporal and hypothetical reasoning, opportunistic control, automatic partitioning and scheduling and both blackboard and distributed problem solving paradigms