A framework for ICAI systems based on inductive inference and logic programming

The main components of an Intelligent Computer-Assisted Instruction (ICAI) system are the expertise, the student model and tutoring strategies. The student model manages what the student dose and dose not understand, and the performance of an ICAI system depends largely on how well the student model approximates the human student. We propose a new framework for ICAI systems which uses the inductive inference for constructing the student model from the student’s behavior. In the framework, both the expertise and the student model are represented as Prolog programs, which enables to express the meta-knowledge that is the knowledge of how to use the knowledge. Since the construction of the student models is performed independently of the expertise, the framework is domain-independent. Therefore, an ICAI system for any subject area can be built with the framework. As an example, the ICAI system teaching chemical reaction is presented together with a sample performance. The authors believe that the new framework for ICAI systems based on logic programming and inductive inference could be a breakthrough of the future ICAI systems.     

智能教学系统中认知型学生模型的建立

在智能教学以学为主的学习模式中,学生模型是提供真正智能化指导的基础,该文提出在基于网络的智能教学系统中建立认知型学生模型的一种方法,介绍基于这种认知型学生模型的一个基于网络的小学数学智能教学系统,并对该系统的实验结果进行分析。     

Student modelling using plan recognition

Abstract

We present a student modelling approach based on plan recognition methods. In some domains, like theorem proving, the student's activity can be seen as consisting of the formation of plans (the proofs) and the execution of actions (the proof steps). Starting from the student's inputs and the problem's search space, the method infers the most plausible plan according to a criterion of coherence. Recognising the student's plan can help predict his next actions and provide him with well-adapted assistance. This modelling technique is applied in an intelligent tutoring system (ITS) which coaches a student during geometry problem-solving. We describe the architecture of the system: the expert, a set of geometry rules together with a theorem prover which can solve problems in different ways and recognise the student's errors; the interface; and the pedagogical module. Finally, we describe the implemented system and its evaluation.     

基于认知理论和随机开放性测试的动态学生建模研究

论文从认知理论出发,提出了一种实用的学生建模方法,即以信息模型和学习模型的组合形式进行学生建模,并采用认知模型、覆盖模型和测试模型对学习模型进行描述。该学生模型可以很好地实现学生模型的动态修改以及教学策略的自适应调整。文中对基于随机算法的开放性测试进行了研究,给出了它的实现算法,并进一步讨论了基于此算法的学生模型修改策略。最后,讨论了基于认知理论与随机开放性测试的动态学生模型在ICAI中的实践,建立了一个ICAI集成教学系统。     

ICAI系统学生模型的设计

本文介绍在ＩＣＡＩ系统中学生模型的作用，并以ＲｅＧＩＳ系统为例，详细介绍了学生模型的具体设计及实现。     

An application of interpretive structural modelling to analyse preference structure in multiobjective dynamic environmental-economic systems†

This paper applies ISM (interpretive structural modelling) to analyse preference structure in a multiobjective problem of a dynamic environmental-economic model, and terms this method IPSM (interpretive preference structural modelling). Multi-objectives in the model include minimizing social cost of pollution, and maximizing total consumption in a planning interval. IPSM analyses decision-maker's preference relations (mental model) among many Pareto-optimal solutions of the multiobjective systems under the interactive cooperation between the decision-maker and the model-builder, and extracts the decision-maker's preference structure as a hierarchical directed graph, where the vertices of the graph correspond to the Pareto-optimal solutions and the directed edges correspond to the preference relations. An interesting economic interpretation for a preferred solution is discussed in terms of the economic interpretation of the discrete minimum principle.     

ICAI系统中知识模型和推理

根据ICAI中知识的特点,文章提出了知识网络图和模糊规则图的知识表示形式,并且将模糊思想引入到ICAI系统中,以优化系统的推理机制,使系统的推理过程更接近人类的思维过程,推理的结果也更为合理、有效。     

Augmenting Node-Link Diagrams with Topographic Attribute Maps

We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.     

ANALYZING MULTIPLE FAULTS IN SYNCHRONOUS SEQUENTIAL CIRCUITS BY BOOLEAN DIFFERENCE TECHNIQUES

The Boolean difference is a mathematical concept that has proved its usefulness in the study of single and multiple “stuck-at” faults in combinational circuits. Goldstein has extended his tool of analysis to cover the multiple stuck-at faults Asynchronous sequential circuits. In this paper, modifications to Goldstein's paper are presented, together with a new method for deriving the required shortest test sequence to detect a specified multiple fault.     

Relating research to curriculum and course development

One problem with research on how students learn (using new technology) is that it is usually concerned with a specific topic, i.e. it has rather a narrow domain and it is not clear to what extent the findings are applicable to other domains. This paper discusses research in two rather different domains: novices learning programming and school pupils interpreting cartesian graphs. We discuss the findings in each domain but our main focus is to examine the extent to which general principles and issues apply across both domains. Finally, we discuss some of the implications from our research, for instructional design.In the case of novices learning programming we are concerned with the development of instructional materials for teaching computer concepts. In the case of graph interpretation the research shows that interpreting trends in graphs is a complex skill which is often performed badly by pupils—but which teachers often assume they can do. Here we are concerned with how computers can be used to help pupils to develop graph interpretation skills and basic concepts.In order to gain a detailed understanding of the learning processes involved in both domains we are using Artificial Intelligence techniques to model the cognitive processes. We shall, therefore, also discuss how such techniques can help us improve instructional design.     

Using Meta-Modelling and Graph Grammars to Create Modelling Environments

This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools, as we store (meta-)models as graphs, and thus, express model manipulations as graph grammars.We show the design and implementation of these concepts in AToM³ (A Tool for Multi-formalism, Meta-Modelling). As an example we will present a meta-model for Causal Block Diagrams and a graph grammar to generate OOCSMP code, a continuous simulation language which has a compiler able to generate Java applets from the simulations models.     

基于Web的《计算机文化基础》智学教学研究浅探

传统的计算机辅助教学(CAI)模式忽视学生的主体性和差异性,不能根据学生的各自特点实施因材施教。而智能计算机辅助教学系统(ICAI)坚持"以人为本、学生第一"的教育原则,尊重学生的个体差异性,实现因人施教、自适应性教学,充分发挥学生的主体作用。本文针对《计算机文化基础》教学中存在的不足,结合智能教学系统特点,研究并给出了一种基于Web的《计算机文化基础》的智能教学系统构造原型。     

Meta-modelling and graph grammars for multi-paradigm modelling in AToM<Superscript>3</Superscript>

This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools: edit, simulate, transform into another formalism, optimize and generate code. We store all (meta-)models as graphs, and thus, express model manipulations as graph grammars.We present the design and implementation of these concepts in AToM³ (A_To_ol for M_ulti-formalism, M_eta-M_odelling). AToM³ supports modelling of complex systems using different formalisms, all meta-modelled in their own right. Models in different formalisms may be transformed into a single common formalism for further processing. These transformations are specified by graph grammars. Mosterman and Vangheluwe [18] introduced the term multi-paradigm modelling to denote the combination of multiple formalisms, multiple abstraction levels, and meta-modelling. As an example of multi-paradigm modelling we present a meta-model for the Object-Oriented Continuous Simulation Language OOCSMP, in which we combine ideas from UML class diagrams (to express the OOCSMP model structure), Causal Block Diagrams (CBDs), and Statecharts (to specify the methods of the OOCSMP classes). A graph grammar is able to generate OOCSMP code, and then a compiler for this language (C-OOL) generates Java applets for the simulation execution.     

基于自适应图学习的多目标特征选择算法

针对多目标回归中的特征选择问题,提出一种基于自适应图学习的多目标特征选择算法,在单个框架中同时考虑3种关系结构:输入特征与目标输出、不同目标输出以及样本间的相关结构,并基于上述结构信息进行特征选择.首先,在传统稀疏回归模型中对系数矩阵施加低秩约束,利用低秩学习对特征间相关性以及目标间的依赖关系进行解耦学习;然后,构建基于样本局部结构信息的自适应图学习项,充分利用样本间的相似结构进行特征选择;进一步地,引入基于输出相关性的结构矩阵优化项,使模型能够更加充分地考虑目标间的相关性;最后,提出一种交替优化算法求解目标函数,并从理论上证明算法的收敛性.在公开数据集上的实验表明,所提方法相较于现有主流的多目标特征选择方法具有更好的性能和适用性.     

A methodology to support multidisciplinary model-based water management

Quality assurance in model based water management is needed because of some frequently perceived shortcomings, e.g. a lack of mutual understanding between modelling team members, malpractice and a tendency of modellers to oversell model capabilities. Initiatives to support quality assurance focus on single domains and often follow a textbook approach with guidelines and checklists. A modelling process involves a complex set of activities executed by a team. To manage this complex, usually multidisciplinary process, to guide users through it and enhance the reproducibility of modelling work a software product has been developed, aiming at supporting the full modelling process by offering an ontological knowledge base (KB) and a Modelling Support Tool (MoST). The KB consists of a generic part for modelling, but also parts specific for various water management domains, for different types of users and for different levels of modelling complexity. MoST's guiding component filters relevant knowledge from the KB depending on the user profile and needs. Furthermore, MoST supports different types of users by monitoring what they actually do and by producing customized reports for diverse audiences. In this way MoST facilitates co-operation in teams, modelling project audits and re-use of experiences of previous modelling projects.     

On the purpose of Event-B proof obligations

Event-B is a formal modelling method which is claimed to be suitable for diverse modelling domains, such as reactive systems and sequential program development. This claim hinges on the fact that any particular model has an appropriate semantics. In Event-B, this semantics is provided implicitly by proof obligations associated with a model. There is no fixed semantics though. In this article we argue that this approach is beneficial to modelling because we can use similar proof obligations across a variety of modelling domains. By way of two examples we show how similar proof obligations are linked to different semantics. A small set of proof obligations is thus suitable for a whole range of modelling problems in diverse modelling domains.     

ICAI系统学习者建模的研究

本文研究智能计算机辅助教学系统（ICAI）学习者建模的方法及特点，分析因特网环境的ICAI系统面临的主要困难；根据系统各部分的主要目标，将各种方法有机结合并突出各自优势的策略，以达到提高系统的综合智能，改善人机接口的目的。     

基于SEAD任务特性约束的协同任务分配方法

以多异构无人机执行SEAD任务为背景,开展协同任务分配问题建模、算法设计和仿真分析.采用图论的方法完成问题的建模,将无人机本体等效为Dubins Car模型,并对其在相应目标处执行侦查、打击、评估任务时的进入角度进行约束,通过Dubins路径完成对无人机飞行路径的等效,采用分布式遗传算法完成对问题的快速求解.研究结果表明,带有路径末端角度约束的任务分配问题具有较好的实用意义,分布式遗传算法可有效处理实时任务分配问题,完成任务空间的快速决策.