A synthesis of fuzzy rule-based system verification.

The verification of fuzzy rule bases for anomalies has received increasing attention these last few years. Many different approaches have been suggested and many are still under investigation. In this paper, we give a synthesis of methods proposed in literature that try to extend the verification of clasical rule bases to the case of fuzzy knowledge modelling, without needing a set of representative input. Within this area of fyzzy V&V we identify two dual lines of thought respectively leading to what is identified as static and dynamic anomaly detection methods. Static anomaly detection essentially tries to use similarity, affinity or matching measures to identify anomalies wihin a fuzzy rule base. It is assumed that the detection methods can be the same as those used in a non-fuzzy environment, except that the formerly mentioned measures indicate the degree of matching of two fuzzy expressions. Dynamic anomaly detection starts from the basic idea that any anomaly within a knowledge representation formalism, i.c. fuzzy if-then rules, can be identified by performing a dynamic analysis of the knowledge system, even without providing special input to the system. By imposing a constraint on the results of inference for an anomaly not to occur, one creates definitions of the anomalies that can only be verified if the inference pocess, and thereby the fuzzy inference operator is involved in the analysis. The major outcome of the confrontation between both approaches is that their results, stated in terms of necessary and/or sufficient conditions for anomaly detection within a particular situation, are difficult to reconcile. The duality between approaces seems to have translated into a duality in results. This article addresses precisely this issue by presenting a theoretical framework which anables us to effectively evaluate the results of both static and dynamic verification theories.

An eclectic quadrant of rule based system verification: work grounded in verification of fuzzy rule bases.

In this paper, we used a research approach based on grounded theory in order to classify methods proposed in literature that try to extend the verification of classical rule bases to the case of fuzzy knowledge modeling. Within this area of verification we identify two dual lines of thought respectively leading to what is termed respectively static and dynamic anomaly detection methods. The major outcome of the confrontation of both approaches is that their results, most often stated in terms of necessary and/or sufficient conditions are difficult to reconcile. This paper addresses precisely this issue by the construction of a theoretical framework, which enables to effectively evaluate the results of both static and dynamic verification theories. Things essentially go wrong when in the quest for a good affinity, matching or similarity measure, one neglects to take into account the effect of the implication operator, an issue that rises above and beyond the fuzzy setting that initiated the research. The findings can easily be generalized to verification issues in any knowledge coding setting.Systems;

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the Freedom space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the seventh in a series of progress updates and covers the period between April 1, 1988 and September 30, 1988. NASA has accepted the basic recommendations of ATAC for its Space Station Freedom efforts. ATAC and NASA agree that the thrust of Congress is to build an advanced automation and robotics technology base that will support an evolutionary Space Station Freedom program and serve as a highly visible stimulator, affecting the U.S. long-term economy. The progress report identifies the work of NASA and the Freedom study contractors. It also describes research in progress, and it makes assessments of the advancement of automation and robotics technology on the Freedom space station

Integration of analysis techniques in security and fault-tolerance

This thesis focuses on the study of integration of formal methodologies in security protocol analysis and fault-tolerance analysis. The research is developed in two different directions: interdisciplinary and intra-disciplinary. In the former, we look for a beneficial interaction between strategies of analysis in security protocols and fault-tolerance; in the latter, we search for connections among different approaches of analysis within the security area. In the following we summarize the main results of the research

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Refinement in response to validation.

Knowledge-based systems (KBSs) are being applied in ever increasing numbers. In parallel with the development of knowledge acquisition tools is the demand for mechanisms to assure their quality, particularly in safety critical applications. Quality assurance is achieved by checking the contents of the KBS at various stages throughout its life cycle. But how does testing for quality assurance aggravate the already well-known knowledge acquisition bottleneck? The partial automation of checking and correcting the knowledge base (KB) is an obvious approach to reducing the bottleneck, but also a more routine treatment of checking will provide improved facilities for quality assurance. In addition to identifying the occurrence offaults, this paper suggests that responding to faults identified by validation is both useful and important. Therefore, refinement should be thought of as a companion to validation