The intelligent help system COMFOHELP

The paper is concerned with the question of whether and under what conditions active help systems with plan recognition components that have been developed in the environment of artificial intelligence research are able to prove their value in the real context of commercial application programs. The question is investigated using the development of the COMFOHELP intelligent help system as an example. COMFOHELP supports the COMFOTEX graphical text processing program and has been developed by the Linguistic Information Science Group at the University of Regensburg since 1988. The system recognizes erroneous and suboptimal plans pursued by the user by analyzing the dialog history and comparing them with the correct plan for achieving the user's goal.Section 2 discusses the research situation and elaborates on those problems which up to now prevented research concepts for plan recognition and intelligent help systems from being practically applied. Testing error situations empirically is a first prerequisite since potential erroneous plans can only be established in real-world tests. The second prerequisite is a special system architecture which counteracts the problem of ambiguities in plan recognition. Section 3 introduces a first still restricted prototype version of COMFOHELP whose efficiency was verified in a statistical hypothesis test. The users performing their text processing tasks with the support of COMFOHELP came off significantly better than members of a reference group working without the intelligent help. Section 4 shows that the proposed COMFOHELP system architecture is reconfirmed by the results of extensive empirical investigations (with more than 100 users) of erroneous plans when using a more complex version of COMFOTEX. The architecture still proves to be worthwhile even when functionality is increased by a factor of three to four.     

Knowledge engineering for an intelligent case-based system for help desk operations

Help desks are computer-aided environments in customer support centers that provide frontline support to external and internal customers. The paper reports on an automated help desk system developed at an information technology company. With the proliferation of diverse software and hardware, the center provides support to a large variety of client systems. The number of calls increases while the turnover rate of employees is high, which means the cost of training escalates. The objective of this project is to develop an automated case-based help desk system to support both call center personnel and customers. The system would contribute to shortening the response time on incoming calls and reduce training time for new employees. The focus of the paper is on the knowledge engineering process of the system. We discuss in detail the knowledge acquisition, knowledge representation, system implementation and verification processes, and we emphasize the structured and automated development methods adopted.     

An adaptive Student Centered Curriculum for an intelligent training system

An intelligent tutoring system customizes its presentation of knowledge to the individual needs of each student based on a model of the student. Student models are more complex than other user models because the student is likely to have misconceptions. We have addressed several difficult issues in reasoning about a student's knowledge and skills within a real-time simulation-based training system. Our conceptual framework enables important aspects of the tutor's reasoning to be based upon simple, comprehensible representations that are the basis for a Student Centered Curriculum. We have built a system for teaching cardiac resuscitation techniques in which the decisions abouthow to teach are separated from the decisions aboutwhat to teach. The training context (i.e., choice of topics) is changed based on a tight interaction between student modeling techniques and simulation management. Although complex student models are still required to support detailed reasoning about how to teach, we argue that the decision about what to teach can be adequately supported by qualitatively simpler techniques, such as overlay models. This system was evaluated in formative studies involving medical school faculty and students. Construction of the student model involves monitoring student actions during a simulation and evaluating these actions in comparison with an expert model encoded as a multi-agent plan. The plan recognition techniques used in this system are novel and allow the expert knowledge to be expressed in a form that is natural for domain experts.     

Expertech: Issues in the design and development of an intelligent help desk system

As more companies turn to expert systems to support traditional help desk activities, the need to understand the particular issues inherent in the development of such systems becomes increasingly important. This article describes the design and development of Expertech, a prototype expert system intended to assist a help desk operator in the resolution of common problems encountered by the users of a telecommunications network. Expertech, a rule-based system, is a partial implementation of a model of human expertise in network diagnosis. We argue that developers of intelligent help desk support systems (IHDSS) must consider several challenges not typically encountered in other environments. An IHDSS, for example, must consider the heterogeneous nature of the end users, and tailor its interface to encompass the varying technical knowledge of those users. Furthermore, an IHDSS must deal with the three-party nature of computer-based support, where the system does not interact directly with the end user. Instead, communication between the IHDSS and the end user takes place indirectly, through a human intermediary, the help desk operator. In the context of these considerations, we describe the approach we took in developing Expertech.     

Student modeling and assessment in intelligent tutoring of software patterns

This paper presents the design, implementation, and evaluation of a student model in DEPTHS (Design Pattern Teaching Help System), an intelligent tutoring system for learning software design patterns. There are many approaches and technologies for student modeling, but choosing the right one depends on intended functionalities of an intelligent system that the student model is going to be used in. Those functionalities often determine the kinds of information that the student model should contain. The student model used in DEPTHS is a result of combining two widely known modeling approaches, namely, stereotype and overlay modeling. The model is domain independent and can be easily applied in other learning domains as well. To keep student model update during the learning process, DEPTHS makes use of a knowledge assessment method based on fuzzy rules (i.e., a combination of production rules and fuzzy logics). The evaluation of DEPTHS performed with the aim of assessing the system’s overall effectiveness and the accuracy of its student model, indicated several advantages of the DEPTHS system over the traditional approach to learning design patterns, and encouraged us to move on further with this research.     

Towards an intelligent system for failure effects analysis

The effectiveness of a firm's design processes is dependent upon the validity of its design theories. The designs of products, processes and procedures embody implicit or explicit theories about the nature of real world processes which shape their performance. The performance of a firm's value creating systems will meet expectations only to the extent that the theories embodied in its designs are comprehensive and valid. Design theories can be evaluated and refined through “knowledge refinement cycles”. Knowledge which is relevant to the evaluation of design theories may be resident in the expertise, experience, databases and formal models of the firm's organizational units. Systems can be designed that focus the application of this knowledge and reduce the costs of access. By refining design theories, the firm can reduce the number of latent flaws in its designs.

Failure effects analysis (FEA) techniques can be applied to support the refinement of the firm's design theories. FEA techniques focus the attention of a designer on the identification of specified classes of latent contingencies in a design. The firm's ability to define specific goals for FEA and to use the results of FEA analyses as measures of design quality enhances managerial control of the design process. In addition, a system for FEA is a common language which supports the integration of organizational knowledge in the evaluation of design alternatives. The power of a system for failure effects analysis can increased through the design of intelligent systems which amplify its strengths and compensate for its weaknesses.     

Speeding up global optimization with the help of intelligent supervisors

It is shown in the paper that Developmental Genetic Programming is an efficient tool for evolutionary development of intelligent supervisors that solve an extension of Resource-Constrained Project Scheduling Problem. The extension assumes that resources are only partially available. It also assumes that renewable resources affect the project cost. The cost should be as low as possible and a deadline of the project must be met. This is apparent with regard to software houses and building enterprises. Computational experiments showed that supervisors find solutions of the problem much faster than other genetic approaches. A specific property of the supervisor is that it has various strategies of allocating the resources to the tasks. The supervisor uses the strategies in order to develop a procedure for producing the best schedule for the whole project. The analysis of the evolutionary process was performed and experimental results were compared with the optimal ones obtained by means of the exhaustive search method.     

Student engagement with mobile-based assessment systems: A survival analysis

Research on mobile-based assessment systems is still an emerging topic in the mobile learning field. Current research has demonstrated that the use of mobile-based assessment systems seems to have a positive impact on students' learning outcomes and motivation. The paper identifies some factors that influence student engagement with mobile-based formative assessment in a language learning course. Survival analysis of the interaction of N = 86 students from eight English as a Foreign Language (EFL) courses over 5 weeks showed that students with higher levels of self-reported effort and perceived importance engaged for longer periods of time. Perceived ease of use, perceived usefulness and behavioural intention to use were all predictors of the longer use of mobile-based assessment systems. We found that within the first 25–50 min of use, about half of the students might disengage from using a mobile-based assessment application.     

Evaluation of an intelligent seat system

This study was conducted to evaluate the effect of an intelligent seat system, a microprocessor-based interactive seat that automatically adjusts itself to fit a seated individual by making pressure-sensitive adjustments on its own. First, a standard American automobile seat ('baseline' seat) was assessed for comfort. Subjective ratings of comfort, pressure distribution and seated anthropometric measurements were recorded for 20 test subjects. These measurements were recorded while the subjects maintained a simulated driving position in a seat buck. The comfort scale was based on a rating of 1 to 10, with 1 corresponding to 'very poor' and 10 corresponding to 'very good.' Based on a nonlinear, multiple regression model that had been previously developed, the comfort rating of the seat was predicted based on the subjective ratings and the recorded values of 450 pressure measurements from 20 subjects. The predicted comfort value was 7.46 for the baseline seat. Following the baseline assessment, the intelligent seat system was installed into the standard American automobile seat. The objective and subjective assessments were then repeated for 17 subjects and the new predicted comfort rating was 8.06. A t-test performed on the subjective and objective measures indicated that this was a significant improvement in seat comfort. Overall, subjects felt the self-adjusting seat was more customized and more comfortable, providing a better fit.     

An intelligent simulation system for earthquake disaster assessment

This paper presents an intelligent simulation system for an earthquake disaster assessment system based on a development platform of a Geographic Information System (GIS) and Artificial Intelligence (AI). This system is designed to identify the weakness of the structure and infrastructure system in pre-earthquake conditions, quickly assess earthquake damage and make an intelligent emergency response for the public and government during the earthquake and post-earthquake. The system includes the following functions: intelligent seismic hazard assessment, earthquake damage and loss evaluation, optimizing emergency response and post-earthquake recovering plan. The principle, design criteria, structure, functions and test results of this system are described in this paper. Based on its functional characteristics, this system is composed of four parts: an information database, analytical modules, an intelligent decision-making sub-system and a friendly user interface. There are 132 coverages and 78 analytical modules included in the information database and analytical modules. With this system, seismic disaster mitigation strategies can be verified during a pre-earthquake, and be executed at the time of an earthquake and post-earthquake; the earthquake resisting capacities for an entire city and all of its communities can be greatly enhanced. To check its reliability and its efficiency, this system has been tested based on a scenario earthquake event in one city, and the related results have also been given in this paper. At the present, this system has been installed and used in Daqing City, China. After running for almost 10 years, this system has successfully been used in rehearsing of seismic disaster mitigation and post-earthquake emergency response. Simultaneously, an optimizing aseismic retrofitting plan in Daqing City has been executed based on results from this system.     

An intelligent questionnaire analysis expert system

In the questionnaire analysis, how to find a statistically significant difference between two or more groups in a continuous measure is one of the major problems in researches. However, it is difficult for researchers to solve the issue of finding possible statistically significant difference. There are two causes of this issue. The one is that the process of finding the statistically significant differences is highly dependent on researchers’ intuition and experience, and the other is that the original questionnaire data may not be good enough to find the statistically significant differences.In this paper, we build a data warehouse and a forward-chaining rule-base expert system with three kinds of indicators, Increase, StepDown, and Dice, for drilling down the data warehouse to assist researchers in exploring the data to select appropriate statistics methods to find possible significant differences. The prototype of this expert system has been implemented, and the results of experiment about satisfaction survey showed finding the significant difference becomes easier, and users were interested in the idea of this system.     

应变式智能称重系统设计

采取STM32单片机和CD4052模块为主要器件构成智能称重系统,该系统由称重传感器、CD4052多路选择开关模块、放大电路、STM32单片机构成,研究并使用了二标准实时自校法,使三步测量过程中零点与灵敏度保持恒定不变.经测试,传统称重传感器的测量误差大约为1.22％,智能化后误差降为0.33％,测量精度得到大幅提高.     

Implementation of an intelligent digital switchboard system

In this paper, an artificial neural network (NN) is used to improve a conventional electric switchboard. The NN used in this paper has two purposes. First, it is used to detect electrical sparks from damaged insulation of electric wire by decrepitude or line damage in indoor electric wire. Second, an autoregressive (AR) NN is used to predict the electrical load in a month so that the management of energy can be administered effectively. This ARNN is used for prediction and learning associated with an on-line process. The data to produce the initial weights of the NN for electrical load forecasting are obtained from the electrical load data of the previous year. The learning through the ARNN is achieved by an off-line process. Because the adaptation period of a NN can be considerably long when random initial weights are used, off-line learning is induced, thereby decreasing the prediction time. After learning, the NN weights are set to the initial values and the NN is implemented to predict the electrical load. It is confirmed that the method developer in this paper has better performance than the conventional one.     

The evaluation of an intelligent tutoring system

Abstract

Numerous intelligent tutoring systems (ITSs) have been developed to date. However, one aspect of their development which is consistently ignored, judging from the literature, is the crucial activity of evaluation. It is also ironical that researchers generally agree that the benefits of this activity could be far-reaching. ITSs are often described to some degree of detail but an appraisal as to their (potential) usefulness is seldom given. The relative novelty of doing this was the main motivation for this paper, which starts by attempting to shed some light on why evaluation has become such a taboo activity, not only in intelligent tutoring, but in artificial intelligence (AI) research in general. It then overviews a tutor, the fractions intelligent tutoring system (FITS), and reports on how it was appraised. The approaches used are neither ideal nor generally accepted, but may well provide an adequate starting point in the belief that an attempt at an honest evaluation of any sort is better than no evaluation at all.     

粮情智能决策支持系统研究与设计

在粮情测控软件平台的基础上,研究开发了粮情分析智能决策支持系统.通过与系统交互,系统可向用户报告粮堆状况、是否安全以及预测粮情变化趋势,辅助用户在不同的情况下采取相应的防护和处理措施.本文主要介绍说明系统的目标、功能、结构和关键技术,并举例分析其应用.     

智能工厂工控系统安全风险评估

智能制造是中国制造业转型升级的必由之路,而基于智能制造理念建设的智能工厂,其最核心的支撑架构则是工业控制系统网络。由于工控网络的特殊性,在建设之初对安全风险的评估往往不足,导致其存在安全隐患。因此首先从智能工厂工控系统网络安全的发展现状入手,进而表达了对工控系统网络安全风险评估问题进行研究的必要性和迫切性,然后从风险评估的概念、评估的方法、评估的基本流程、评估的实施、风险值的计算和风险等级划分等方面对工控系统安全风险评估的策略做了一些探索性的总结。     

物联网中的智慧溯源服务系统Petri网建模与分析

物联网（IOT）为人类社会的智慧化进程提供了革命性的信息技术,已广泛应用于社会各个领域。智慧农业是物联网的重要应用领域之一,农产品溯源是智慧农业的一个典型应用。在学习Petri网理论的基础上,围绕物联网在智慧农业领域的应用实际,结合农产品溯源系统需求,设计了一个基于物联网技术的农产品智慧溯源服务系统架构,分析了系统的整体业务流程,建立了系统的Petri网模型。采用关联矩阵和不变量对所建模型的可达性、有界性、安全性和活性等性质进行了分析,使用PIPE工具进行了仿真验证,证明了模型系统的合理性、可靠性和健壮性。     

I-DSS: an intelligent diagnostic support system

An intelligent diagnostic support system (I-DSS) for decision-making support in diagnostic processes is presented. I-DSS can be placed between diagnosis carried out by a human diagnostician, without any automatic support, and diagnosis carried out in a fully automatic way. Fully automatic diagnosis may be appealing if used in very complex domains and if the user is non-expert. However, in the case of an expert user, a fully automatic approach is not suitable. In the fully automatic approach the system should be equipped with a strategic knowledge base (the knowledge needed for making the 'best' choice) and as a consequence the expert user is prevented from making decisions on the basis of his or her own experience. This restriction causes, in general, a sort of psychological rejection, on the part of the expert user, of the traditional fully automatic approach. This is particularly true in those domains, such as medicine, where there is more than one approach to the solution and it is seldom that one approach can be considered 'right' and the others 'wrong'. Experience related to diagnostic expert systems applications shows that, whenever trade-off problems arise in choosing between alternative actions, it is preferable to leave decisions to the expert. Starting from these considerations we present a system (I-DSS) which, without being 'intrusive', aims to be an effective support for the decision-maker during the diagnostic process.     

InParS: an experimental intelligent parallelization system

In this paper we discuss the design and implementation of an intelligent program parallelization system, called InParS. This system in based on intelligent parallelization models proposed by many researchers in the area of parallelizing compilers. The presented experiment is one of few attempts toward investigating the viability of artificial intelligence techniques in automatic program parallelization. The early version of InParS was aimed at transforming Fortran-like DO loops into a vector code well-suited for vector processors. The new version of InParS targets distributed memory parallel computers. Some preliminary research results are also presented, which give an indication of how incorporating artificial intelligence techniques can contribute towards the success of automatic program parallelization.