Section: Overall Objectives
Design and model acquisition issues
When designing a dependable and adaptive system, a main point is to formally characterize the intended properties of the system such as the diagnosability (i.e. whether, given the system specifications, it is possible to detect and explain an error in due time), or the repairability (i.e. whether it is possible to get the system back to correctness, in due time). Moreover, these two properties must be combined to get the best compromise, so as to get actual self-healing systems. Some of these concepts have been defined, but in a centralized context. We are interested in extending the solutions proposed so far for discrete-event systems in the decentralized context.
It is well recognized that model-based approaches suffer from the difficulty of model acquisition. The first issue we have studied is the automatic acquisition of models from data with symbolic learning methods and data mining methods. The problems which are investigated are listed here. How to improve relational learning methods to cope efficiently with data coming from signals (as electrocardiograms in the medical domain) or alarm logs (in the telecommunication domain)? How to integrate signal processing algorithms to the learning or diagnosis tasks when these latter ones rely on a qualitative description of signals? How to adapt the learning process to deal with multiple sources of information (multi-sensor learning)? How to apply learning techniques to spatio-temporal data? How to combine data mining and visualization to help experts build their models?
Concerning evolving context management and adaptive systems, an emerging issue is to detect when a model is becoming obsolete and to update it by taking advantage of the current data. This difficult and new issue is related to data streams processing and is highly challenging in the monitoring research area where the model is used as a reference by the diagnosis task.
The last point we consider is the decision part itself, mainly the ability of proposing repair policies in order to restore the functionalities of the system or the expected quality of service. A first direction is to interleave diagnosis and repair and to design some decision-theoretic procedure to dynamically choose the best action to undertake. Another direction is to be able to automatically build the recommending actions from simulation or recorded data.
