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.     

感觉神经Petri网模型及知识表示*

以普通Petri网和模糊Petri网为基础,给出了感觉神经Petri网的模型和基本定义,并讨论了其在知识表示中的应用问题。     

A general framework for reasoning about change

The capability to represent and use concepts like time and events in computer science is essential to solve a wide class of problems characterized by the notion of change. Real-time, databases and multimedia are just a few of several areas which needs good tools to deal with time. Another area where this concepts are essential is artificial intelligence because an agent must be able to reason about a dynamic environment. In this work a formalism is proposed which allows the representation and use of several features that had been recognized as useful in the attempts to solve such class of problems. A general framework based on a many-sorted logic is proposed centering our attention in issues such as the representation of time, actions, properties, events and causality. The proposal is compared with related work from the temporal logic and artificial intelligence areas. This work complements and enhances previously related efforts on formalizing temporal concepts with the same purpose. Juan Carlos Augusto, Ph.D.: He is a Lecturer in the Department of Computer Science at Universidad Nacional del Sur (Argentina), where he graduated as Licenciado en Ciencias de la Computacion and Doctor en Ciencias de la Computacion. Currently on leave in the Department of Electronics and Computer Science, University of Southampton (United Kingdom). His research interests are focused in the dynamic aspects of computing systems. This involves solving conceptual problems related to the specification of time and change and designing tools to improve systems in several areas of computer science, such as artificial intelligence, databases, multimedia, software verification and real-time systems. He has been conducting research on temporal representation and reasoning since 1993. Throughout these years he had the opportunity to contribute to several research projects as a researcher and has head or co-head of research groups. Other activities and contributions to highlight are the organization of international events, editorial work and supervision of postgraduate students, all of which contributes to the generation and dissemination of knowledge about the dynamic aspects of computing systems.     

An intellectual celebration: A review of the jurix legal knowledge based systems scholarship

The Foundation for Legal Knowledge Systems (JURIX) has, sinceits foundation in 1988, become an internationally renowned forumfor Law and Artificial Intelligence in theNetherlands. This paper is based onan intellectual review of the work of JURIX requested by theorganisation as part of its 10th anniversary in December 1997 andpresented as a keynote address at the 10th anniversary conference.It has been updated to include the following two conferences. Itapplauds the overall effort but also suggests some directions forfuture development and suggests in particular: The breadth of research has been commendable, butthere is a need to move towards greater integration of systems andmigration to internet based systems. Tools are likely to be improved in the next generationincluding the internet. Legal Knowledge Based Systems (LKBS) researchmay need to abandon fine boundaries between AI and other applications tools. The commendable diversity of approaches to legalknowledge representation overlays concerns about the nature of lawand its translation in LKBS. These issues of knowledge representation, theory, costand organisation of production can be best addressed by a return to acybernetic systems theory as a basis of analysis of relationships involved,and an understanding of the implications of culture change. An awareness that traditional legal theories were notmeant for the information age. Theorisation about law needs to beinformed by a range of new theoretical approaches which approachlegal systems as cultural systems. There is need to study the impact on legal cultures ofthe development of knowledge based systems, that is the impact ofjurimetricisation of society.     

以知识点为中心的智能教学系统的研究与构建

在深入研究智能教学系统知识表示方法的基础上,提出了一个以知识点为中心的智能教学系统构建方案,采用知识点作为教学资源的组成单位,通过知识点间的联系来构建知识体系,并结合Web挖掘、人工智能、模糊理论等技术,实现网络教学的智能教学策略。     

Defaults as restrictions on classical Hilbert-style proofs

Since the earliest formalisation of default logic by Reiter many contributions to this appealing approach to nonmonotonic reasoning have been given. The different formalisations are here presented in a general framework that gathers the basic notions, concepts and constructions underlying default logic. Our view is to interpret defaults as special rules that impose a restriction on the juxtaposition of monotonic Hubert-style proofs of a given logicL. We propose to describe default logic as a logic where the juxtaposition of default proofs is subordinate to a restriction condition . Hence a default logic is a pair (L, ) where properties of the logic , like compactness, can be interpreted through the restriction condition . Different default systems are then given a common characterization through a specific condition on the logicL. We also prove cumulativity for any default logic (L, ) by slightly modifying the notion of default proof. We extend, in fact, the language ofL in a way close to that followed by Brewka in the formulation of his cumulative default system. Finally we show the existence of infinitely many intermediary default logics, depending on and called linear logics, which lie between Reiter's and ukaszewicz' versions of default logic.Work carried out in the framework of the agreement between Italian PT Administration and FUBLaforia, Université Paris VI Pierre et Marie Curie, 4 Place Jussieu,Tour 46, 75252 Paris, France     

人工智能综述

人工智能是研究使计算机来模拟人的某些思维过程和智能行为的学科,主要包括计算机实现的智能的原理、制造类似于人脑智能的计算机,使计算机能实现更高层次的应用。     

浅谈人工智能的应用与发展

本文主要介绍了人工智能的基本理论体系和该技术的应用概况,追索其理论发展历程,并对其主要的发展方向进行了技术上的评述.     

智能教学系统的发展与前瞻

该文回顾了ITS的发展历史,并通过对影响ITS发展的相关学科的分析,做出了对ITS未来的预测。     

The representation of legal contracts

The paper outlines ongoing research on logic-based tools for the analysis and representation of legal contracts, of the kind frequently encountered in large-scale engineering projects and complex, long-term trading agreements. We consider both contract formation and contract performance, in each case identifying the representational issues and the prospects for providing automated support tools.     

A computational model of an intuitive reasoner for ecosystem control

Intuition is the human capacity to make decisions under novel, complex situations where knowledge is incomplete and of variable levels of certainty. We take the view that intuition can be modeled as a rational and deductive mode of information processing which is suited to novel, complex situations. In this research, a computational algorithm, or “intuitive reasoner”, is proposed which mimics some aspects of human intuition by combining established mathematical tools, such as fuzzy set theory, and some novel innovations. A rule-based scheme is followed and a rule-learning module that allows rules to be learned from incomplete datasets is developed. The input and the rules drawn by the reasoner are allowed to be fuzzy, multi-valued, and low in certainty. A measure of the certainty level, Strength of Belief, is attached to each input as well as each rule. Solutions are formulated through iterations of consolidating intermediate reasoning results, during which the Strength of Belief of corroborating intermediate results is combined. An experimental implementation of the proposed intuitive reasoner is reported, in which the reasoner was used to solve a classification problem. The results showed that, when given increasingly sparse input data, the rule-learning module generated more rules of lower associated certainty than when presented with more complete data. The intuitive reasoner was able to make use of these low-certainty rules to solve the classification problems with an accuracy that compared favorably to that of traditional methods based on complete datasets.     

基于人工智能的金融监管信息系统

本文介绍了人工智能和知识工程的有关技术，并针对我国金融监管的现状，讨论了如何建立有效的基于人工智能的金融监管信息系统进行风险 评估和预警。     

An artificial intelligence system of trouble diagnosis for aircraft engines

:Reported here is about the trouble diagnosis system for AN-24 aircraft engine which has been realized by inputting the experiences of the repair mechanics or experts of the engine as a computer software.The system is composed of following four sections which are called “model” ; a phenomena model, an inference model, a learning model, and an interpretation model.Therefore, the system is called as “model diagnosis system”. These four models are relatively independent which makes parallel operation, easy debugging, and addition of new knowledge possible.

The experience of the engine experts has been stored initially to outer knowledge base in the computer. Intermidiate knowledge which arises on the process of the inference is treated at inner knowledge base. The inner knowledge base adopts a blackboard structure. This makes the system not only able to diagnose the vague preconditioned reason, but also to diagnose the unpreconditioned one by learning. The validity of the system was proved from some experiments.     

美国国防部联合人工智能中心建设情况及经验分析

联合人工智能中心是美国国防部推动人工智能发展和军事应用的核心机构,中心下设战略政策部、能力发展部、任务部、规划预算采办部等业务部门,中心的运行贯穿了人工智能应用全生命周期,重点聚焦短期项目执行与人工智能技术应用工作,经过多年发展,中心在跨机构协调、业务管理模式优化、技术环境和工具手段建设、内部文化建设、高端人才引进等方面积累了诸多经验,值得分析和借鉴。     

中国象棋的编程设计

本文阐述了中国象棋编程设计．首先叙述了象棋中的位量与下棋策略的关系．为算法设计提供基础：其次。简述了α-β搜索技术．并对其提出了相应方法来减小搜索时间和计算夏杂度。最后也指出了现有算法的不足和改进方向。     

基于元路径与层次注意力的时序异质信息网络表示学习方法

异质信息网络表示学习在节点分类、链接预测、个性化推荐等多个领域上被广泛应用.现有的异质信息网络表示学习方法大多集中在静态网络,忽略网络中时间属性对节点表示的影响.为了解决该问题,文中提出基于元路径和层次注意力的时序异质信息网络表示学习方法.利用元路径捕获异质信息网络中的结构和语义信息.通过时间衰减注意力层,捕获不同元路径实例在特定时间对目标节点的影响.通过元路径级别注意力,融合不同元路径下的节点表示,得到最终表示.在DBLP、IMDB数据集上的实验表明,文中方法在节点分类和节点聚类任务上均可达到较优效果.     

基于多维信息融合的知识库问答实体链接

知识库问答实体链接任务需要将问句内容精准链接到知识库中实体.当前方法大多难以兼顾链接实体的召回率和精确率,并且仅能根据文本信息对实体进行区分筛选.因此,文中在合并子步骤的基础上,提出融合多维度特征的知识库问答实体链接模型(MDIIEL).通过表示学习方法,将文本符号、实体和问句类型、实体在知识库中语义结构表达等信息整合并引至实体链接任务中,加强对相似实体的区分,在提高准确率的同时降低候选集的大小.实验表明,MDIIEL模型在实体链接任务性能上具有整体性提升,在大部分指标上取得较优的链接结果.     

多专家系统中管理和控制结构的研究

对一般多专家系统的管理和控制问题,提出了一个新的概念:管理控制知识,它是多专家系统的特有知识,用于对多个专家系统的合作和单专家系统的执行过程等进行管理和控制。该文分析了管理控制知识的描述定义、分类、获取和表示方法。     

基于人工智能的计算机故障诊断系统研究

以人工智能的理论为基础.根据计算机故障诊断专家系统的实践经验,以及收集了大量计算机故障诊断资料,从计算机不同的故障特征建立了诊断指标,开发计算杌故障诊断专家系统.结果表明.该指标科学合理、有效.可以进行故障诊断,在现实生活中具有广泛的应用价值.