融合机器学习与知识推理的可解释性框架

针对基于规则的可解释性模型可能出现的规则无法反映模型真实决策情况的问题, 提出了一种融合机器学习和知识推理两种途径的可解释性框架. 框架演进目标特征结果和推理结果, 在二者相同且都较为可靠的情况下实现可解释性. 目标特征结果通过机器学习模型直接得到, 推理结果通过子特征分类结果结合规则进行知识推理得到, 两个结果是否可...     

Mining for knowledge in databases: The INLEN architecture,initial implementation and first results

The architecture of an intelligent multistrategy assistant for knowledge discovery from facts, INLEN, is described and illustrated by an exploratory application. INLEN integrates a database, a knowledge base, and machine learning methods within a uniform user-oriented framework. A variety of machine learning programs are incorporated into the system to serve as high-levelknowledge generation operators (KGOs). These operators can generate diverse kinds of knowledge about the properties and regularities existing in the data. For example, they can hypothesize general rules from facts, optimize the rules according to problem-dependent criteria, determine differences and similarities among groups of facts, propose new variables, create conceptual classifications, determine equations governing numeric variables and the conditions under which the equations apply, deriving statistical properties and using them for qualitative evaluations, etc. The initial implementation of the system, INLEN 1b, is described, and its performance is illustrated by applying it to a database of scientific publications.     

Decomposition of information systems for production management

This paper approaches the issue of decentralization and decomposition of information systems from two angles, viz. from an organizational and from an infological point of view. Current information systems tend to become more and more integrated. However, this integration causes organizational complexity, which, in turn, becomes prohibitive for organizational change. Thus, there is a need for decomposition of the information system from an organizational point of view. A strategy for such a decomposition in a production environment is given.     

Knowledge management for consumer-focused product design

As the automotive industry adopts a consumer focus in its product development strategy, it offers broader product ranges, shorter model lifetimes and the ability to process orders in arbitrary lot sizes. This offers the ability to conduct early product design and development trade-off analysis among these competing objectives. A distributed knowledge-based system, which analyzes, verifies, stores, and retrieves process definitions, is needed to manage the complexity of workflows. The use of information technologies and networking capabilities is essential in the dissemination of product knowledge in order to integrate the decision-making process among heterogeneous and distributed partners/units. This paper offers insights into a knowledge management approach that enables implementing a consumer-focused product design philosophy by integrating capabilities for intelligent information support and group decision-making utilizing a common enterprise network model and knowledge interface through shared ontologies. An automotive supply chain case study is utilized in illustrating the proposed approach.     

Knowledge management by example

Knowledge has many components such as data, constraints, queries, transactions, and derivation rules. Data is the only component that can be managed effectively in large quantities. All other components are in their infancy in terms of tools and techniques for efficient storage and retrieval, implementation and execution, and user specification and design. One approach to manage all components of knowledge in large quantities is to reduce them all to data. Many components of knowledge can be expressed in terms of examples, and examples are data. As such, all these components can be stored and retrieved efficiently in large quantities, their execution reduces to data comparison and can be done in parallel, and they can be specified, designed, and modified by end users since examples are more intuitive and easy to manipulate than general procedures.     

Database management and analysis tools of machine induction

This paper surveys machine induction techniques for database management and analysis. Our premise is that machine induction facilitates an evolution from relatively unstructured data stores to efficient and correct database implementations.     

Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications

Machine Learning is an area concerned with the automation of the process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge based systems use higher level knowledge representations. The method we propose here, provides a technique which automatically allows us to extract production rules from the lower level representation used by a single-layered neural networks trained by Hebb's rule. Even though a single-layered neural network can not model complex, nonlinear domains, their strength in dealing with noise has enabled us to produce correct rules in a noisy domain.     

基于模拟退火算法的知识获取方法的研究

从优化角度提出了从事例中获取知识的机器学习方法。该方法利用模拟退火算法,按照预定的优化目标,从事例中生成最优的产生式规则,给出其算法,并以旋转机械故障诊断知识获取为例,阐述了基于模拟退火算法的知识获取机制及其实现方法。     

Imputation of Missing Data in Industrial Databases

A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.     

中小型软件企业知识管理的研究

越来越多的我国中小型软件企业认识到员工的知识是企业最有价值的资产,而且在企业中实施知识管理是十分必要的。首先概括了这些企业在知识管理方面的现状,提出了一个适用于中小型软件企业的知识管理体系,说明了知识管理与CMM／CMMI的关系,并给出了知识管理与软件过程管理的结合模型,介绍了一个软件企业知识管理系统的模型框架,分享了实施知识管理的一些经验。     

Knowledge discovery from industrial databases

The proliferation of large masses of data has created many new opportunities for those working in science, engineering and business. The field of data mining (DM) and knowledge discovery from databases (KDD) has emerged as a new discipline in engineering and computer science. In the modern sense of DM and KDD the focus tends to be on extracting information characterized as knowledge from data that can be very complex and in large quantities. Industrial engineering, with the diverse areas it comprises, presents unique opportunities for the application of DM and KDD, and for the development of new concepts and techniques in this field. Many industrial processes are now automated and computerized in order to ensure the quality of production and to minimize production costs. A computerized process records large masses of data during its functioning. This real-time data which is recorded to ensure the ability to trace production steps can also be used to optimize the process itself. A French truck manufacturer decided to exploit the data sets of measures recorded during the test of diesel engines manufactured on their production lines. The goal was to discover knowledge in the data of the test engine process in order to significantly reduce (by about 25%) the processing time. This paper presents the study of knowledge discovery utilizing the KDD method. All the steps of the method have been used and two additional steps have been needed. The study allowed us to develop two systems: the discovery application is implemented giving a real-time prediction model (with a real reduction of 28%) and the discovery support environment now allows those who are not experts in statistics to extract their own knowledge for other processes.     

极小极大规则学习及在决策树规则简化中的应用

文中在粗糙集理论中的约简概念的启发下提出极小规则和极大规则的概念及极小极大规则学习。     

航空机载软件缺陷知识库框架

提高航空机载软件质量成为当前一个亟须解决的问题.建立软件缺陷知识库对于进行有效的软件质量评价及软件故障预测,识别易于出现缺陷的软件模块,提高软件测试效率和软件质量,都能起到重要作用.提出了一个基于机器学习和产生式系统推理相结合的航空机载软件缺陷知识库构建方法和相应的框架,该框架还包含软件缺陷度量元选取标准、选取清单,以及缺陷信息统计要求、分析方法.在此框架的基础上,利用实际测评工作中积累的大量航空机载软件缺陷数据,构建了一个统一、规范的软件缺陷知识库,并通过该知识库给出了缺陷预防信息,从而对航空机载软件全寿命周期进行了有效指导.     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

实现人工神经网络知识增殖能力的一种方法

具有知识增殖能力的神经学习系统是人工神经网络发展的一个重要方向,备受研究人员的关注．传统上对神经学习系统知识的增殖或重用研究偏重于对个体网络的改造,根据知识积累和继承的思想,引入自治神经网络(autonomous artificial neural network,AANN)的理念,以此作为构造知识可增殖神经学习系统的基础,利用群体网络的方法成功解决了神经学习系统的拓展和知识增殖问题．AANN和一般神经网络的区别在于其自治能力,采用AANN模块构造的神经学习系统,具有知识增殖能力,其可靠性、可拓展性和灵活性都得到提高．实验结果表明,该方法构造的群体网络系统可有效继承其模块所学习的知识．     

Knowledge Management in Societies of Intelligent Adaptive Agents

A model is developed of the emergence of the knowledge level in asociety of agents where agents model and manage other agents as resources,and manage the learning of other agents to develop such resources. It isargued that any persistent system that actively creates the conditions forits persistence is appropriately modeled in terms of the rationalteleological models that Newell defines as characterizing the knowledgelevel. The need to distribute tasks in agent societies motivates suchmodeling, and it is shown that if there is a rich order relationship ofdifficulty on tasks that is reasonably independent of agents then it isefficient to model agents competencies in terms of their possessingknowledge. It is shown that a simple training strategy of keeping an agent'sperformance constant by allocating tasks of increasing difficulty as anagent adapts optimizes the rate of learning and linearizes the otherwisesigmoidal learning curves. It is suggested that this provides a basis forassigning a granularity to knowledge that enables learning processes to bemanaged simply and efficiently.     

利用类比知识的基于解释的学习

本文介绍如何将示例中包含的新的知识通过基于解释的学习加入到原来不完善的领域知识库中去。整个学习过程是在示例的引导下依据领域理论和类比知识进行推理的纯演绎过程。因此，经过改进的领域理论可以保持其正确性。系统的原型在ＳＵＮ工作站上用ＱＵＩＮＴＵＳ ＰＲＯＬＯＧ实现。