Structuring Decisions Under Deep Uncertainty

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature

Enhanced Task Modelling for Systematic Identification and Explicit Representation of Human Errors

International audienceTask models produced from task analysis, are a very important element of UCD approaches as they provide support for describing users goals and users activities, allowing human factors specialists to ensure and assess the effectiveness of interactive applications. As user errors are not part of a user goal they are usually omitted from tasks descriptions. However, in the field of Human Reliability Assessment, task descriptions (including task models) are central artefacts for the analysis of human errors. Several methods (such as HET, CREAM and HERT) require task models in order to systematically analyze all the potential errors and deviations that may occur. However, during this systematic analysis, potential human errors are gathered and recorded separately and not connected to the task models. Such non integration brings issues such as completeness (i.e. ensuring that all the potential human errors have been identified) or combined errors identification (i.e. identifying deviations resulting from a combination of errors). We argue that representing human errors explicitly and systematically within task models contributes to the design and evaluation of error-tolerant interactive system. However, as demonstrated in the paper, existing task modeling notations, even those used in the methods mentioned above, do not have a sufficient expressive power to allow systematic and precise description of potential human errors. Based on the analysis of existing human error classifications, we propose several extensions to existing task modelling techniques to represent explicitly all the types of human error and to support their systematic task-based identification. These extensions are integrated within the tool-supported notation called HAMSTERS and are illustrated on a case study from the avionics domain

An assessment of DREAM, appendix E

The design realization, evaluation and modelling (DREAM) system is evaluated. A short history of the DREAM research project is given as well as the significant characteristics of DREAM as a development environment. The design notation which is the basis for the DREAM system is reviewed, and the development tools envisioned as part of DREAM are discussed. Insights into development environments and their production are presented and used to make suggestions for future work in the area of development environments

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

Using Complementary Models-Based Approaches for Representing and Analysing ATM Systems' Variability

International audienceLarge-Scale Socio-Technical Systems, such as Air Traffic Management (ATM), are organizations where different interconnected systems work together to achieve a common goal. Analysis of variability is particularly challenging in these systems of systems due to the non-linear and complex interactions among social and technical functions. This paper proposes a systematic approach able to represent and to reason about the variability of such socio-technical systems. The proposed approach is based on the synergistic use of 3 models able to represent the variability from different points of view. This federation of models focusses the analysis on the relevant aspects of the systems of systems at different levels of granularity. The models taken into account for the representation of system variability are FRAM [12] focusing on organizational functions, HAMSTERS [17], which is centred on human goals and activities and ICO [20] which is dedicated to the representation of systems' behaviour (including the user interface). The paper presents a detailed development process describing how the models are built and analysed. This process is exemplified on a case study involving the AMAN (Arrival MANager) system