Designing automation for human use: empirical studies and quantitative models

An emerging knowledge base of human performance research can provide guidelines for designing automation that can be used effectively by human operators of complex systems. Which functions should be automated and to what extent in a given system? A model for types and levels of automation that provides a framework and an objective basis for making such choices is described. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design when using the model. Four human performance areas are considered—mental workload, situation awareness, complacency and skill degradation. Secondary evaluative criteria include such factors as automation reliability, the risks of decision/action consequences and the ease of systems integration. In addition to this qualitative approach, quantitative models can inform design. Several computational and formal models of human interaction with automation that have been proposed by various researchers are reviewed. An important future research need is the integration of qualitative and quantitative approaches. Application of these models provides an objective basis for designing automation for effective human use.     

Designing automation for human use: empirical studies and quantitative models

An emerging knowledge base of human performance research can provide guidelines for designing automation that can be used effectively by human operators of complex systems. Which functions should be automated and to what extent in a given system? A model for types and levels of automation that provides a framework and an objective basis for making such choices is described. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design when using the model. Four human performance areas are considered--mental workload, situation awareness, complacency and skill degradation. Secondary evaluative criteria include such factors as automation reliability, the risks of decision/action consequences and the ease of systems integration. In addition to this qualitative approach, quantitative models can inform design. Several computational and formal models of human interaction with automation that have been proposed by various researchers are reviewed. An important future research need is the integration of qualitative and quantitative approaches. Application of these models provides an objective basis for designing automation for effective human use.     

Systematic testing of an integrated systems model for coastal zone management using sensitivity and uncertainty analyses

Systematic testing of integrated systems models is extremely important but its difficulty is widely underestimated. The inherent complexity of the integrated systems models, the philosophical debate about the model validity and validation, the uncertainty in model inputs, parameters and future context and the scarcity of field data complicate model validation. This calls for a validation framework and procedures which can identify the strengths and weaknesses of the model with the available data from observations, the literature and experts’ opinions. This paper presents such a framework and the respective procedure. Three tests, namely, Parameter-Verification, Behaviour-Anomaly and Policy-Sensitivity are selected to test a Rapid assessment Model for Coastal-zone Management (RaMCo). The Morris sensitivity analysis, a simple expert elicitation technique and Monte Carlo uncertainty analysis are used to facilitate these three tests. The usefulness of the procedure is demonstrated for two examples.     

A review of: “LOGIC AND PHILOSOPHY IN THE LVOV-WARSAW SCHOOL”, by Jan Woleński,Kluwer, Dordrecht (The Netherlands) and Boston, 1989. xiv + 364 pages.

Qualitative methodology plays an important role within computer simulation; modeling and analysis of complex systems require qualitative methods since humans think naturally in qualitative and linguistic terms. The critical interface for simulationists exploring qualitative simulation should rely on an unambiguous mathematical formalism or method with foundations in systems theory. Currently, many ad hoc formalisms exist for encoding uncertain or qualitative simulation knowledge; however, we have found that fuzzy set theory provides for a formalism where linguistic variables can be encoded as state, parameter, input and output information in the model. Fuzzy numbers, in particular, are useful when population statistics are unavailable—usually due to cost factors. We have constructed fuzzy simulation programs based on our C-based SimPack library and we use fuzzy simulation to hypothesize qualitative system models reflecting real system behavior, and to specify qualitative versions of systems.     

连续系统故障可诊断性评价方法综述

故障可诊断性评价是故障诊断的基础, 能够为诊断算法的开发和传感器的优化配置提供依据与指导. 在梳 理可诊断性概念、评价流程与功能的基础上, 从定量模型、定性模型以及数据驱动这3 个方面对连续系统可诊断性 评价方法进行分类综述, 归纳和评析了各方法的原理与特点, 并比较了定性评价与量化评价结果的优缺点. 最后探讨了故障可诊断性评价方法未来可能的发展方向.     

Intelligent systems for traffic flow management: A qualitative modeling approach

An approach for the design of intelligent systems for traffic control is presented. First, the problem characteristics are described. Then, the possible knowledge representation techniques are proposed. the types of knowledge to be represented are:

• — The data interpretation knowledge for problem identification based on the present and predicted situations.
• — The knowledge for proposing control decisions to solve present and predicted problems.
• — The knowledge for possible short-term situations prediction with the present control policy assumed as constant.

The first two types can be represented by traditional techniques such as rules or frames. the predictive knowledge has to be defined on qualitative modeling techniques, an aspect of intensive research nowadays. Two types of predictive modeling techniques are suggested: (a) the qualitative simulation of flow along an axis which is the case of the flow in urban motorways where it is very important the preservation of an operational capacity. A confluence version is proposed for the partial derivative equations of unsteady traffic flow in a lane. (b) the qualitative modeling of the flow in networks where a qualitative version of the flow assignment to paths in the network is used. A proposal of knowledge representation for every prediction type is described. Although specific aspects of traffic control are presented the approach discussed can be considered as a general knowledge representation concept which combines traditional techniques and qualitative models in order to infer decision aspects for management purposes in a professional engineering domain. Finally, some knowledge structuring and acquisition aspects are considered.     

Model formulation as a problem-solving task: Computer-assisted engineering modeling

A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. © 1993 John Wiley & Sons, Inc.     

CAUSAL MODELS AS INTELLIGENT LEARNING ENVIRONMENTS FOR SCIENCE AND ENGINEERING EDUCATION

AI research in qualitative physics and causal models suggests new approaches to teaching people about science and engineering. We have been investigating the form that such models need to take to be effective within intelligent learning environments. The subject matter we have focused on is understanding how electrical circuits work, but the approach can be generalized to other subjects. Two key hypotheses have emerged from our research. The first is that in order to understand a physical system, students need to acquire causal mental models for how the system works. Further, it is not enough to have just a single mental model; students need alternative mental models that represent the systems behavior from different but related perspectives, such as at the macroscopic and microscopic levels. The second hypothesis is that in order to make causal understanding feasible in the initial stages of learning, students have to be introduced to simplified models. These models are then gradually refined into more sophisticated mental models. The questions addressed in this article are: What are the properties of an easily learnable, coherent set of initial models? What are the types of evolutions needed for students to acquire a more powerful set of models with broad utility?     

面向链接预测的知识图谱表示学习方法综述

作为人工智能的重要基石, 知识图谱能够从互联网海量数据中抽取并表达先验知识, 极大程度解决了智能系统认知决策可解释性差的瓶颈问题, 对智能系统的构建与应用起关键作用. 随着知识图谱技术应用的不断深化, 旨在解决图谱欠完整性问题的知识图谱补全工作迫在眉睫. 链接预测是针对知识图谱中缺失的实体与关系进行预测的任务, 是知识图谱构建与补全中不可或缺的一环. 要充分挖掘知识图谱中的隐藏关系, 利用海量的实体与关系进行计算, 就需要将符号化表示的信息转换为数值形式, 即进行知识图谱表示学习. 基于此, 面向链接预测的知识图谱表示学习成为知识图谱领域的研究热点. 从链接预测与表示学习的基本概念出发, 系统性地介绍面向链接预测的知识图谱表示学习方法最新研究进展. 具体从知识表示形式、算法建模方式两种维度对研究进展进行详细论述. 以知识表示形式的发展历程为线索, 分别介绍二元关系、多元关系和超关系知识表示形式下链接预测任务的数学建模. 基于表示学习建模方式, 将现有方法细化为4类模型: 平移距离模型、张量分解模型、传统神经网络模型和图神经网络模型, 并详细描述每类模型的实现方式与解决不同关系元数链接预测任务的代表模型. 在介绍链接预测的常用的数据集与评判标准基础上, 分别对比分析二元关系、多元关系和超关系3类知识表示形式下, 4类知识表示学习模型的链接预测效果, 并从模型优化、知识表示形式和问题作用域3个方面展望未来发展趋势.     

Model-based reasoning: a principled approach for software engineering

The software engineering industry suffers from almost unmanageable complexity both in the products it produces and in the processes of production. One of the current shortcomings in the software production process is the weakness of the models used. This paper makes observations on the role of knowledge in engineering and examines the central role of models and simulation. We develop an argument for the application of certain new forms of modelling methods in software engineering in order to impose more discipline and give a principled framework for building models that can support the software life-cycle. The concept of a model is examined in depth and different characteristics and types of model are defined. This introduces the relatively new concept of qualitative models and their use in the field known as model-based reasoning. Unlike previous knowledge-based methods, model-based reasoning has several important advantages. Although very few model-based software projects exist, we illustrate how this approach can be developed by drawing on applications from traditional engineering. It is argued that, because qualitative modelling offers great power for addressing the issue of complexity, such models have considerable potential as high-level abstractions of software products. These could form the core of tools for the management and support of the software development process through the whole product life-cycle.     

Process modelling for intelligent control

This contribution considers the problem of knowledge representation in intelligent control systems: the kind of knowledge that is required and how this may be encapsulated within an application framework. It is argued that rule-base techniques need to be supplemented by other methods in order to express certain pertinent aspects of the domain. After a brief discussion of qualitative modelling techniques, attention is focused upon numerical models and a relatively new qualitative modelling approach, Qualitative Transfer Functions.

The effectiveness of the latter model forms as repositories of deep knowledge are evaluated by application to a simulated distillation plant. The results indicate that although numerical models can provide accurate input-output representations, the integrated framework offered by Qualitative Transfer Functions is capable of carrying more information relevant to decision-making systems.     

A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems

Fuzzy Cognitive Maps (FCM) are a promising approach for socio-ecological systems modelling. FCMs represent problem knowledge extracted from different stakeholders in the form of connected factors/variables with imprecise cause-effect relationships and many feedback loops. These typically large maps are condensed and aggregated to obtain a summary view of the system. However, representation, condensation and aggregation of previous FCM models are qualitative due to lack of appropriate quantitative methods. This study tackles these drawbacks by developing a semi-quantitative FCM model consisting of robust methods for adequately and accurately representing and manipulating imprecise data describing a complex problem involving stakeholders for pragmatic decision making. The model starts with collecting qualitative imprecise data from relevant stakeholders. These data are then transformed into stakeholder perceptions/FCMs with different causal relationship formats (linguistic or numeric) which the proposed model then represents in a unified format using a 2-tuple fuzzy linguistic representation model which allows combining imprecise linguistic and numeric values with different granularity and/or semantic without loss of information. The proposed model then condenses large FCMs using a semi-quantitative method that allows multi-level condensation. In each level of condensation, groups of similar variables are subjectively condensed and the corresponding imprecise connections are computationally condensed using robust calculations involving credibility weights assigned to variables (variables’ importance). The model then uses a quantitative fuzzy method to aggregate perceptions/FCMs into a stakeholder group or social perception/FCM based on the 2-tuple model and credibility weights assigned to FCMs (stakeholders’ importance). Thereafter, the structure of produced FCMs is analysed using graph theory indices to examine differences in perceptions between stakeholders or groups. Finally, the model applies various what-if policy scenario simulations on group FCMs using a dynamical systems approach with neural networks and analyses scenario outcomes to provide appropriate recommendations to decision makers. An example application illustrates method’s effectiveness and usefulness.     

A comparative analysis of the usage and infusion of wiki and non-wiki-based knowledge management systems

Antecedents of adoption and diffusion in time-honored models such as the technology acceptance model and innovation diffusion theory may not provide sufficient measures for newer Web 2.0 technologies such as wikis. This research examines two potential extensions to the basic tenets of user acceptance: reciprocity expectation and personal innovativeness in information technology (IT). The research also examines an advancing technology: wiki technology-based knowledge management systems. Based on the results of an online survey, partial least squares analysis is used to evaluate the proposed model and provide comparative results for traditional knowledge management systems and wikis. Of the 170 respondents, 46 indicated wiki-based systems as their primary knowledge management system, while 124 indicated non-wiki-based systems as the primary system. The results indicate the set of factors influencing usage are different than the factors influencing infusion. Further, non-wiki-based versus newer wiki-based knowledge management systems have different sets of factors affecting usage and infusion. Both extensions to the base model have a greater impact on wiki-based systems as opposed to non-wiki-based systems. Reciprocity expectation was found to have a contradictory significant negative influence on infusion of wikis. Additionally, personal innovativeness in IT moderates the usage and infusion of wikis more so than for traditional knowledge management systems. These results support a more robust model for analyzing the utilization of such technologies.     

Knowledge-based parameter identification of TSK fuzzy models

Linear/1st order Takagi–Sugeno–Kang (TSK) fuzzy models are widely used to identify static nonlinear systems from a set of input–output pairs. The synergetic integration of TSK fuzzy models with artificial neural networks (ANN) has led to the emergence of hybrid neuro-fuzzy models that can have excellent adaptability and interpretability at the same time. One drawback of these hybrid models is that they tend to have more black-box characteristics of ANN than the transparency of fuzzy systems. If the quality of training data is questionable then it may lead to a fuzzy model with poor interpretability. In an attempt to remediate this problem, we propose a parameter identification technique for TSK models that relies on a-priori available qualitative domain knowledge. The technique is devised for rule-centered TSK models in which the consequent polynomial can be interpreted as the 1st order Taylor series approximation of the underlying nonlinear function that is being modeled. The resulting neuro-fuzzy model is named as a-priori knowledge-based fuzzy model (APKFM). We have shown that besides being reasonably accurate, APKFM has excellent interpretability and extrapolation capability. The effectiveness of APKFM is shown using two examples of static systems. In the first example, a toy nonlinear function is chosen for approximation by an APKFM. In the second example, a real world problem pertaining to the maintenance cost estimation of electricity distribution networks is addressed.     

A model for the interpretation of verbal predictions

There is a marked gap between the demands on forecasting and the results that numerical forecasting techniques usually can provide. It is suggested that this gap can be closed by the implementation of experts' qualitative predictions into numerical forecasting systems. A formal analysis of these predictions can then be integrated into quantitative forecasts.In the framework of possibility theory, a model is developed which accounts for the verbal judgments in situations where predictions are made or knowledge is updated in the light of new information. The model translates verbal expressions into elastic constraints on a numerical scale. This numerical interpretation of qualitative judgments can then be implemented into numerical forecasting procedures.The applicability of this model was tested experimentally. The results indicate that the numerical predictions from the model agree well with the actual judgments and the evaluation behavior of the subjects.The applicability of this model is demonstrated in a study where bank clerks had to predict exchange rates. The analysis of qualitative judgments according to this model provided significantly more information than numerical predictions.A general framework for an interactive forecasting systems is suggested for further developments.     

A method of qualitative reasoning for model-based problem solving and its application to a nuclear plant

Model-based expert systems are expected to contribute to overcoming the difficulties of conventional rule-based systems. This paper describes the modeling of mechanical systems and a method of qualitative reasoning based on causal specifications. The causal specifications represent a component's local causal properties, following the principles for reusability and composability. It contributes to providing intuitive causal ordering of complex behavior originated in the combination of components, including inter-component negative feedback. A model of a system is represented by combining a set of local component models and global knowledge derived from general properties of the physical entity. This allows for reusable knowledge which is easy to describe. Furthermore, the method has been successfully applied to a nuclear power plant. Reasoning results were unambiguous and matched those obtained by domain experts.     

Weighted modal transition systems

Specification theories as a tool in model-driven development processes of component-based software systems have recently attracted a considerable attention. Current specification theories are however qualitative in nature, and therefore fragile in the sense that the inevitable approximation of systems by models, combined with the fundamental unpredictability of hardware platforms, makes it difficult to transfer conclusions about the behavior, based on models, to the actual system. Hence this approach is arguably unsuited for modern software systems. We propose here the first specification theory which allows to capture quantitative aspects during the refinement and implementation process, thus leveraging the problems of the qualitative setting. Our proposed quantitative specification framework uses weighted modal transition systems as a formal model of specifications. These are labeled transition systems with the additional feature that they can model optional behavior which may or may not be implemented by the system. Satisfaction and refinement is lifted from the well-known qualitative to our quantitative setting, by introducing a notion of distances between weighted modal transition systems. We show that quantitative versions of parallel composition as well as quotient (the dual to parallel composition) inherit the properties from the Boolean setting.     

Qualitative modelling with imprecise ecological knowledge: a framework for simulation

Ecological understanding is often imprecise and heterogeneous; relationships between different quantities and objects may only be expressed in roughly quantitative or even non-quantitative terms. We argue that there is a need for general time-driven simulation modelling systems capable of utilising these types of understanding, using vegetation dynamics as an example. Although work has gone into developing qualitative models in the past, it has not focussed on the needs of time-driven simulation and there is currently no off-the-shelf solution available. This paper presents a categorisation of the types of knowledge that comprise formal models, then uses the categorisation as the basis for exploring and developing a framework for time-driven simulation modelling with imprecise, heterogeneous knowledge. One of the key concepts presented is the explicit separation of all non-quantitative state variable values from their direction and rate of change. From this concept a general computational method is developed for updating non-quantitative state variables in time-driven simulation. First-order logic is advocated as a suitable representational vehicle and a modelling system that implements the proposed framework is briefly presented. We believe that the framework provides a useful step towards increasing the practical utility of available knowledge.