基于web的智能教学系统研究

该文对智能教学系统进行了深入的研究,提出了由用户环境和教学环境组成的基于web的智能教学系统形式化模型,并且对系统运行过程进行了详细的分析。该模型将本体论引入到领域知识的表示中提高了教学资源的重用和共享程度;同时,充分考虑了web以及用户环境所提供的与用户相关信息在智能教学系统中的重要作用。系统基于多领域多应用,结合认知能力、知识水平、学习风格、心理特征等知识结构和认知心理因素来对用户进行建模以增强系统的自适应能力和提高教学效果。     

A multi-functional knowledge based system to learn, apply and consult procedures

One of the main challenges facing systems developers is to design systems with the flexibility and adaptability required to satisfy a professional user's needs. This paper presents a novel architecture and a knowledge representation scheme for a multi-functional adaptive system that support professional engineers that work with established procedures. The system achieves its multi-functionality by using a single knowledge representation scheme based on a set of modular networks. The developed representation is exploited by three operators that enable a user to learn, consult different procedures and solve problems. The representation also facilitates the automatic generation of problems and the identification of user errors. The integration of functionalities through a single representation produces a synergy that results in extra-functionality, flexibility and reduces the amount of development effort required. The system adapts to a user by using simple but efficient user modelling techniques that tailor the tutoring information. A rule-based mechanism is used to propose an agenda and a novel rule simplification algorithm is developed to help an expert to develop the rules for proposing the agenda. A prototype was developed and three civil engineering procedures were implemented to evaluate the architecture through scenarios and an empirical evaluation with real engineers.     

基于神经网络的知识获取

本文提出了用基于规则专家系统与神经网络的集成，该系统实现了从实例中自动获取知识的功能．在产生和控制不完全情况方面提高了专家系统的推理能力．它使用无导师学习算法的神经网络来获取正规数据，并用一个符号生成器把这些正规的数据变换成规则．生成规则和训练后的神经网络作为知识库嵌于专家系统中．在诊断阶段，为了诊断不明情况，可同时使用知识库和人类专家的知识，而且系统可以利用训练过的神经网络的综合能力进行诊断，并使不相符数据完整化．     

Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems

Expert scheduling systems, which develop the schedule automatically on a real time basis, are able to respond to the changes of product demand in Flexible Manufacturing Systems (FMS). While developing an expert scheduling system, the most time-consuming and difficult step is knowledge acquisition, the process that elicits the knowledge from experts and transfers it into the knowledge base. A trace-driven knowledge acquisition (TDKA) method is proposed to extract the expertise from the schedules produced by expert schedulers. Three phases are involved in the TDKA process: data collection, data analysis, and rule evaluation. In data collection, the expert schedulers are identified and decisions made during the scheduling process are recorded as a trace. In data analysis, a set of scheduling rules is developed based on the trace. The rules are then evaluated in the last phase. If the resulting rules do not perform as well as the expert schedulers, the process returns to phase two and refines the rules. The whole process stops whenever the resulting rules perform at least as well as the expert schedulers. A circuit board production line is used to demonstrate the feasibility of the TDKA methodology. The scheduling rules perform much better than the expert schedulers from whom the rules are extracted.     

An expert system interface for an identification program

This paper describes an expert system interface, named (ihs), for the interactive data analysis and system identification program Idpac. The interface works as an intelligent help system. The system is completely noninvasive and uses the previous command history to understand what the user is doing and gives help according to this. This way of monitoring the user's activities is called the command spy strategy. Scripts are used for representing procedural knowledge, and production rules for diagnostic knowledge. The system has been implemented and a knowledge database handling system identification with the maximum-likelihood method has been developed. An example run with the system is included.     

Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor PAT2Math

In order for an Intelligent Tutoring System (ITS) to correct students’ exercises, it must know how to solve the same type of problems that students do and the related knowledge components. It can, thereby, compare the desirable solution with the student’s answer. This task can be accomplished by an expert system. However, it has some drawbacks, such as an exponential complexity time, which impairs the desirable real-time response. In this paper we describe the expert system (ES) module of an Algebra ITS, called PAT2Math. The ES is responsible for correcting student steps and modeling student knowledge components during equations problem solving. Another important function of this module is to demonstrate to students how to solve a problem. In this paper, we focus mainly on the implementation of this module as a rule-based expert system. We also describe how we reduced the complexity of this module from O(n^d) to O(d), where n is the number of rules in the knowledge base, by implementing some meta-rules that aim at inferring the operations students applied in order to produce a step. We evaluated our approach through a user study with forty-three seventh grade students. The students who interacted with our tool showed statistically higher scores on equation solving tests, after solving algebra exercises with PAT2Math during an approximately two-hour session, than students who solved the same exercises using only paper and pencil.     

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

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.     

3M: a user modelling interface of an expert system for X-ray topographic image interpretation

This paper discusses user modelling techniques and presents the design and implementation of the 3M user modelling interface of INTEREX. INTEREX is an expert system for X-ray topographic image interpretation which assists its users in identifying and analysing a number of defects that can occur in high-quality crystals. 3M consists of a monitor, a model and a modifier. It is used to adapt the consultation route and the explanations provided by INTEREX to three categories of users. It demonstrates the use-of an implicit, individual, dynamic and long-term user model to enable an expert system to accommodate users with different levels of expertise.     

Intelligent tutoring systems: Formalization as automata and interface design using neural networks

This article proposes a mathematical model of Intelligent Tutoring Systems (ITS), based on observations of the behaviour of these systems. One of the most important problems of pedagogical software is to establish a common language between the knowledge areas involved in their development, basically pedagogical, computing and domain areas. A mathematical model, like the one proposed here, can facilitate the integration of these different areas, as it defines the elements that constitute the system and defines the technological tools to implement it. The article presents an example demonstrating how the formalization was used to design the adaptive mechanism of an ITS to adapt its Interface Module to some student characteristics.     

L-CATA: A logic-based expert travel system

L-CATA (Logic-based Computer Aided Travel Assistant) is a logic-based expert database system, which asks the user to input his query specification, such as starting place, destination, constraints, rules and goals, etc., and outputs a list of flights meeting the traveller's specification; together with an alternative list which may not quite meet the user's specification but optimizes his goals. L-CATA is written as a deductive database system, and uses heuristic rules to prune its search of the database. Unlike other air-travel related expert systems, L-CATA does not attempt to model the traveller. Instead, L-CATA complements existing Computer Reservation Systems by providing comprehensive individually tailored advice and information to the traveller. There are several approaches to implement such a system. The logic approach is a very promising one, and the aims of L-CATA can be more easily achieved by using it. In this paper, we present a logic approach to the L-CATA expert database system, and provide a theoretical foundation for such a database system.     

Expert System Hardware for Fault Detection

This paper focuses upon the development of three new electronic architectures of inference engines as a part of a hardware expert system applied to very high-speed faults detection in industrial processes. The architecture of this expert system consists of an inference engine (a dedicated processor that is necessary due to the high-speed requirements and the repetitiveness of the operation), which uses a pattern-directed inference system; a fact base, which stores the status of the signals at each moment, and a static knowledge base, which contains the inference rules compiled from expert knowledge. A circuit for analyzing time is also presented. This allows time to be taken as another variable of the process and carries out a redundancy analysis simultaneously with the fault detection module.     

专家系统中基于粗集的知识获取、更新与推理

知识获取、知识更新和不确定性推理是设计专家系统的重要方面。根据粗集理论，提出了一种专家系统的结构模型，该系统在规则获取的基础上，利用系统运行的实例增量式地更新知识库中的规则及其参数，以改善系统的性能，利用知识库中的规则及数量参数进行不确定性推理，得出结论的可信度。     

THE USEFULNESS OF A MACHINE LEARNING APPROACH TO KNOWLEDGE ACQUISITION

This paper presents results of experiments showing how machine learning methods arc useful for rule induction in the process of knowledge acquisition for expert systems. Four machine learning methods were used: ID3, ID3 with dropping conditions, and two options of the system LERS (Learning from Examples based on Rough Sets): LEM1 and LEM2. Two knowledge acquisition options of LERS were used as well. All six methods were used for rule induction from six real-life data sets. The main objective was to lest how an expert system, supplied with these rule sets, performs without information on a few attributes. Thus an expert system attempts to classify examples with all missing values of some attributes. As a result of experiments, it is clear that all machine learning methods performed much worse than knowledge acquisition options of LERS. Thus, machine learning methods used for knowledge acquisition should be replaced by other methods of rule induction that will generate complete sets of rules. Knowledge acquisition options of LERS are examples of such appropriate ways of inducing rules for building knowledge bases.     

An example of expert system on numerical modelling system in coastal processes

With the advent of artificial intelligence technology as well as the widespread popularity of desktop microcomputers in recent years, integration of this new technology with the traditional numerical modelling system becomes a current trend in order to solve various engineering problems. It renders a more intelligent and user-friendly system on the problem domain. In this paper, a knowledge-based expert system on numerical modelling system for coastal water processes is delineated. Expert system application, as a key branch of artificial intelligence technology, is integrated with traditional numerical modelling for simulating flow and water quality phenomenon in coastal waters. The knowledge bases are classified into five major types, namely, a variety of models, relations between various model parameters and real physical conditions, feasible options of model parameters, question base as a user-interface directing the user to depict the actual physical conditions, and the rules of inference deducing the feasible choice of model and its parameters. A hybrid expert system shell, Visual Rule Studio, is employed as an ActiveX Designer under Microsoft Visual Basic environment because it combines the advantages of both production rules and object-oriented programming technology. Both forward chaining and backward chaining are used collectively during the inference process, which is mainly driven by premises and conditions with the highest factors of confidence. The inference engine will drive the decision tree to explore the most probable option of numerical model and parameters matching the real problem specifications. It is shown that the application and integration of the knowledge-based expert system technology into numerical modelling for coastal processes can provide substantial assistance to novice users for selection of numerical model as well as parameters.     

Knowledge based formation of re-configurable assembly cells

When developing assembly cells with highly complex modular structures, designers need to translate user requirements into a set of design rules and potential cell configurations. The success in matching user requirements to potential products is dependent on how well the functional and non-functional customer requirements can be understood and translated into cell features (design rules, processes and module types). This paper reports on a knowledge based methodology for forming customisable re-configurable assembly cells. The approach is based on matching user requirements to existing supplier knowledge in terms of design rules and principles, modules offered by different vendors, new emerging technologies and existing own and competitors’ products. The decision making includes requirements analysis, generating assembly processing alternatives and evaluating and selecting assembly modules and cells. The proposed approach aims to assist decision making in assembly system design by enabling users and suppliers to jointly participate in an interactive and iterative process of forming re-configurable assembly cells.