Revising deductive knowledge and stereotypical knowledge in a student model

A user/student model must be revised when new information about the user/student is obtained. But a sophisticated user/student model is a complex structure that contains different types of knowledge. Different techniques may be needed for revising different types of knowledge. This paper presents a student model maintenance system (SMMS) which deals with revision of two important types of knowledge in student models: deductive knowledge and stereotypical knowledge. In the SMMS, deductive knowledge is represented by justified beliefs. Its revision is accomplished by a combination of techniques involving reason maintenance and formal diagnosis. Stereotypical knowledge is represented in the Default Package Network (DPN). The DPN is a knowledge partitioning hierarchy in which each node contains concepts in a sub-domain. Revision of stereotypical knowledge is realized by propagating new information through the DPN to change default packages (stereotypes) of the nodes in the DPN. A revision of deductive knowledge may trigger a revision of stereotypical knowledge, which results in a desirable student model in which the two types of knowledge exist harmoniously.     

一个用户接口管理系统

本文介绍了一个用户接口管理系统NUUIMS的设计和实现.重点叙述了该系统基于规范化目标用户接口模型的设计思想,系统组成及描述方法.文章也讨论了使用NUUIMS设计而成的用户接口的特点.     

渤中13-1油田井身结构优化研究

针对渤中 1 3- 1油田钻井过程中存在的主要问题 ,利用测井、地质、录井和钻井资料 ,对该油田的井身结构进行研究 ,设计出合理的井身结构 ,优化了套管程序 ,为进一步减少钻井过程中漏、喷、塌、卡等复杂情况的发生 ,提供了科学的理论依据     

C语言学习方法与实践的探讨

针对C语言程序设计课程本身的特点，分析学习过程中存在的问题，强调实践的必要性。结合一些学校课程改革和实践的经验．提出面向应用的，科学的学习内容、方法和模式。同时提高学习者的专业动手能力。     

A classification of tournaments having an acyclic tournament as a minimum feedback arc set

Given a tournament with an acyclic tournament as a feedback arc set we give necessary and sufficient conditions for this feedback arc set to have minimum size.     

Revising threshold functions

A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give an efficient revision algorithm in the model of learning with equivalence and membership queries for threshold functions, and some negative results showing, for instance, that threshold functions cannot be revised efficiently from either type of query alone. The algorithms work in a general revision model where both deletion and addition type revision operators are allowed.     

Toward bridging the science and practice of depression prevention: What can we learn from cognitive vulnerability?

Depression is a widespread disorder with devastating individual and societal consequences. Although a great deal of research and theory has focused on treatment of acute episodes, progress needs to be made in preventing the emergence of first episodes of the disorder. There has been considerable advancement in understanding psychological vulnerability factors associated with this mood disorder, especially on the basis of cognitive behavioural models and research findings based on this theoretical model. In this paper we review the concept of cognitive vulnerability, with a particular focus on what this body of research work suggests clinically for the prevention of depression. We outline, based on this science, what the effective ingredients of a prevention program could be. We also discuss some of the pragmatic aspects of developing an effective prevention program for depression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prolog逻辑程序的测试方法

Prolog逻辑程序设计语言具有不同于传统程序设计语言的特点.本文根据测试Prolog程序的实践,提出了一系列为Prolog程序生成测试用例的准则.按这些准则测试实际程序效果良好.这是在逻辑程序测试新领域迈出的一步.     

The Design of Discrimination Experiments

Experimentation plays a fundamental role in scientific discovery. Scientists experiment to gather data, investigate phenomena, measure quantities, and test theories. In this article, we address the problem of designing experiments to discriminate between two completing theories. Given an initial situation for which the two theories make the same prediction, the experiment design problem is to determine how to modify the situation such that the two theories make different predictions for the modified situation. The modified situation is called a discrimination experiment. We present a knowledge-intensive method called DEED for designing discrimination experiments. The method analyzes the differences in the two theories' explanations of the prediction for the initial situation. Based on this analysis, it determines modifications to the initial situation that will result in a discrimination experiment. We illustrate the method with the design of experiments to discriminate between several pairs of qualitative theories in the fluids domain.