DIAMS revisited: Taming the variety of knowledge in fault diagnosis expert systems

The DIAMS program, initiated in 1986, led to the development of a prototype expert system, DIAMS-1 dedicated to the Telecom 1 Attitude and Orbit Control System, and to a near-operational system, DIAMS-2, covering a whole satellite (the Telecom 2 platform and its interfaces with the payload), which was installed in the Satellite Control Center in 1993. The refinement of the knowledge representation and reasoning is now being studied, focusing on the introduction of appropriate handling of incompleteness, uncertainty and time, and keeping in mind operational constraints. For the latest generation of the tool, DIAMS-3, a new architecture has been proposed, that enables the cooperative exploitation of various models and knowledge representations. On the same baseline, new solutions enabling higher integration of diagnostic systems in the operational environment and cooperation with other knowledge intensive systems such as data analysis, planning or procedure management tools have been introduced

Aggregating models for anomaly detection in space systems: Results from the FCTMAS study

The Flight Control Team Multi-Agent System (FCTMAS) study, funded by the European Space Agency (ESA), has investigated the use of multiagent systems in supporting flight control teams in routine operations. One of the scientific challenges of the FCTMAS study has been the detection of anomalies relative to a space system only on the basis of identified deviations from the nominal trends of single measurable variables. In this paper, we discuss how we addressed this challenge by looking for the best structure that aggregates a given set of models, each one returning the anomaly probability of a single measurable variable, under the assumption that there is no a priori knowledge about the structure of the space system nor about the relationships between the variables. Experiments are conducted on data of the Cryosat-2 satellite and their results are eventually summarized as a set of guidelines

A possibilistic framework for single-fault causal diagnosis under uncertainty

International audienceThis paper presents a general approach to diagnosis in a relational setting where uncertainty is expressed by means of possibility theory. Causal knowledge is supposed to be described by directed links between causes and their possible symptoms. More precisely, symptoms are described on binary or non-binary attribute domains by means of fuzzy sets and may be pervaded with uncertainty. Moreover, observations may be also fuzzy and uncertain. The proposed model generalizes both a previously developed approach to binary symptoms on the one hand, and a treatment of graded symptoms in the case of precise observations also recently introduced on the other hand

Possibility Theory and its applications: a retrospective and prospective view.

International audienceThis paper provides an overview of possibility theory, emphasising its historical roots and its recent developments; Possibility theory lies at the crossroads between fuzzy sets, probability and non-monotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and common-sense reasoning with exception-tainted knowledge in Artificial Intelligence. It has been axiomatically justified in a decision-theoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statiscal reasoning with imprecise probabilities. As such it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information

Representations of Uncertainty in Artificial Intelligence: Probability and Possibility

International audienceDue to its major focus on knowledge representation and reasoning, artificial intelligence was bound to deal with various frameworks for the handling of uncertainty: probability theory, but more recent approaches as well: possibility theory, evidence theory, and imprecise probabilities. The aim of this chapter is to provide an introductive survey that lays bare specific features of two basic frameworks for representing uncertainty: probability theory and possibility theory, while highlighting the main issues that the task of representing uncertainty is faced with. This purpose also provides the opportunity to position related topics, such as rough sets and fuzzy sets, respectively motivated by the need to account for the granularity of representations as induced by the choice of a language, and the gradual nature of natural language predicates. Moreover, this overview includes concise presentations of yet other theoretical representation frameworks such as formal concept analysis, conditional events and ranking functions, and also possibilistic logic, in connection with the uncertainty frameworks addressed here. The next chapter in this volume is devoted to more complex frameworks: belief functions and imprecise probabilities