PMS:一种支持并行推理的模型

目前，许多成功的专家系统都是用产生式系统实现的，但是随着系统知识库规模的不断增长，推理效率却急骤下降．本文在分析了产生式系统的优点和不足的基础上，提出了一种改进方案，介绍一种能在多机环境下进行推理的并行成员系统ＰＭＳ（ＰａｒａｌｅｌＭｅｍｂｅｒＳｙｓｔｅｍ）。     

一台并行知识库机的推理模型和通讯同步机制

并行知识库机是一种用于提高知识库系统推理效率的多机系统。本文利用ＰＯＳＴ模型来形式化它的并行推理模型和操作规范，为保证系统正确性和可靠性、还重点讨论它的通讯－同步机制。     

关于产生式系统并行执行与收敛性问题的探讨

产生式系统引入并行技术后，出现了两个新的困难问题：相容性问题和收敛性问题。基于并行知识库机ＰＫＢＭ９５，为了解决收敛性问题，本文给出了一种称为规则顺序锁定的方法。另外，为了发现隐循环以及为了提高性能，我们给出一系列动态分析了测试的方法，如路径跟踪等等。     

在微机上扩展的并行知识库机

本文提出的并行知识库机ＰＫＢＭ９５的硬件结构是一台微机和四台ＴＲＡＮＳＰＵＴＥＲ组成的多机系统。这里重点讨论了ＰＫＢＭ的系统结构、操作规范和操作语言，并提出一订散式的并行推理模型有诟端机、前端机两次冲突归结策略。     

支持产生式系统并行推理的动态推理网

产生式系统作为人工智能领域的重要分支,已得到较为成熟的应用.同时也不断面临一 些新的需求.针对产生式系统并行处理能力的需求,提出一种基于动态推理网(DRN)的知识 库构造方法,并介绍整个知识库的描述与构造方式,以及相关算法的实现.在此基础上支持产 生式系统的并行推理功能.     

通用法规知识库系统的设计

建立法规知识库系统有助于增强执法公正性,提高执法效率。文章讨论了法规知识库系统的设计思想和系统结构。系统采用广泛使用的产生式系统实现,并对基本的产生式系统作了一些改进,知识库由规则库和已定案例库组成,推理采用二级推理。最后给出了系统存在的问题及解决的方法。     

MTPM—一种在多Transputer系统上实现OPS5并行匹配的方案

并行匹配是并行处理产生式系统的重要内容之一。本文针对现有RETE匹配算法,提出了一种自行设计的在多Transputer系统上实现OPS5并行匹配的方案,并重点讨论了实现MTPM中的一些技术问题.试验表明,MTPM提供了一条发掘产生式系统固有并行性的可能途径.     

并行推理多处理机系统DADO

用传统多处理机系统来作知识库/专家系统中的并行推理或PROLOG程序执行中的并行解释,DADO是较有影响的一个代表。本文介绍DADO的背景与结构,DADO的工作方式,以及DADO用作并行产生式系统机器和并行PROLOG解释系统。     

OPS_5并行点火推理模型MES-1

并行点火是并行处理产生式系统的重要内容之一.本文依据产生式规则的数据依赖图,较详细地剖析了OPS_5并行点火时出现的相关问题,并提出一种相关测试算法以及一种按顺接最大相容子集来循环划分规则的方法,旨在获得最大的并行点火效益.此外,本文还提出了OPS_5并行点火推理模型MES-1以及在von Neumann机上的模拟方法.最后,对MES-1做了评价.     

基于加权产生式规则知识库的不一致性和冗余性研究

一、问题的提出规则的产生式表示法是目前专家系统中最常用的一种方法,它易于表达浅层知识,并且具有模块性、清晰性、自然性等优点。但是,由于产生式系统的知识库常常是不完备的,需要对规则进行增、删、改等维护操作,这往往会引起知识库中知识的不一致性和冗余     

一种产生式系统的并行推理模型

1.引言 在人工智能领域中,产生式系统是一种比较有效的知识表示方法,并得到了广泛应用。目前许多较为成功的专家系统都是用产生式系统实现的~[1]。随着应用领域的不断扩士其知识座的抓橄称渐增     

一个基于分布推理的知识库

随着知识处理量的增大，分布式知识库成了一个很重要的发展方向。本文提出了一个基于分布推理的知识库模型，这个模型允许用户充分利用分布在各处的知识求解问题作查询，并提出了一个优化的问题求解的推理机制。     

Parallel inference in dynamic decision support systems of a semiotic type

The organization of parallel inference in dynamic decision support systems (DDSS) of a semiotic type, oriented towards a solving of ill-formed problems in dynamic applied domains, is considered. As a knowledge representation model, there are used production rules reflecting expert knowledge about a problem domain, an environment and decision making processes. The main concepts and assertions defining possibility and impossibility of parallel executing the production rules are given. Several types of parallelism in an inference process are introduced. The corresponding algorithm of parallel inference is described. Thus, the purpose of this paper is to develop and to research parallel inference methods and procedures that provide efficient processing a large amount of production rules for DDSS of a semiotic type.     

Parallel machine scheduling problems using memetic algorithms

In this paper, we investigate how to apply the hybrid genetic algorithms (the memetic algorithms) to solve the parallel machine scheduling problem. There are two essential issues to be dealt with for all kinds of parallel machine scheduling problems: job partition among machines and job sequence within each machine. The basic idea of the proposed method is that (a) use the genetic algorithms to evolve the job partition and then (b) apply a local optimizer to adjust the job permutation to push each chromosome climb to his local optima. Preliminary computational experiments demonstrate that the hybrid genetic algorithm outperforms the genetic algorithms and the conventional heuristics.     

面向对象机器翻译知识库IMT—KB的设计与实现

机器翻译知识库是机译系统的重要组成部分,针对传统机译知识库的不足之处,本文提出一种面向对象的机译知识体系结构,同时给出这种具有层次性和模块性机译知识库的存储组织和管理机制。     

Parallel inference machines at ICOT

Japan's fifth generation computer systems (FGCS) project aims at the research and development of new computer technology for knowledge information processing system (KIPS) that will be required in 1990s. In this project, logic programming is adopted for the base for software and hardware system to be developed. As a primitive operation of logic programming is syllogistic inference, machines studied and built in the project are called inference machines.

One of the project's target machines is a parallel inference machine (PIM) having about 1000 processing elements. Smaller scale PIMs are also planned as intermediate targets. In addition to PIMs, sequential inference machines (SIMs) have been developed for a software development tool. A personal type SIM is called PSI which is a logic programming workstation. For research and development of parallel software systems, especially, an operating system for PIM (PIMOS), a multi-PSI system which consists of several CPUs of PSI connected with a high-speed network, is also under development. In the intermediate stage plan of the project, parallel software research is emphasized and conducted more systematically.

This paper describes research and development plans for the parallel inference machine in conjunction with the parallel software research.     

Paradigms for knowledge-based systems in industrial engineering

When developing expert systems, expertise lies not only in formulating the knowledge to be put into the knowledge base, but also in deciding upon the knowledge representation and inference mechanism most suited to the application. Six detailed knowledge bases demonstrate the application of various AI-based systems to industrial engineering problems. They illustrate a number of approaches: expert systems, which are based upon practical experience; decision systems, which derive from modelling skills; and situation-action systems, which rely on production process design skills. The six paradigms presented describe a logical expert system for selecting material handling equipment; a multi-valued expert system for selecting a dispatching rule for automatic guided vehicles; a profile matching expert system for selecting project management software; a confidence building expert system for selecting a machine feeder; a tandem decision system for developing a production schedule; and a situation-action system for controlling job allocation in a flexible manufacturing cell. The relationships between these various paradigms and the characteristics of problems to which they can be applied are categorized by the nature of the expert and his expertise; the features of the environment; the decision or decisions to be taken; and the manner in which AI-system performance can be evaluated. A knowledge base is proposed for determining which architecture is most appropriate for a given application.     

An incremental neural learning framework and its application to vehicle diagnostics

This paper presents a framework for incremental neural learning (INL) that allows a base neural learning system to incrementally learn new knowledge from only new data without forgetting the existing knowledge. Upon subsequent encounters of new data examples, INL utilizes prior knowledge to direct its incremental learning. A number of critical issues are addressed including when to make the system learn new knowledge, how to learn new knowledge without forgetting existing knowledge, how to perform inference using both the existing and the newly learnt knowledge, and how to detect and deal with aged learnt systems. To validate the proposed INL framework, we use backpropagation (BP) as a base learner and a multi-layer neural network as a base intelligent system. INL has several advantages over existing incremental algorithms: it can be applied to a broad range of neural network systems beyond the BP trained neural networks; it retains the existing neural network structures and weights even during incremental learning; the neural network committees generated by INL do not interact with one another and each sees the same inputs and error signals at the same time; this limited communication makes the INL architecture attractive for parallel implementation. We have applied INL to two vehicle fault diagnostics problems: end-of-line test in auto assembly plants and onboard vehicle misfire detection. These experimental results demonstrate that the INL framework has the capability to successfully perform incremental learning from unbalanced and noisy data. In order to show the general capabilities of INL, we also applied INL to three general machine learning benchmark data sets. The INL systems showed good generalization capabilities in comparison with other well known machine learning algorithms.