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 joined to get the best compromise for building real self-healing systems. Some of these concepts have been defined, but in a centralized context. We aim at 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. We list the investigated problems here. How to improve relational learning methods to cope efficiently with data coming from signals (as an electrocardiogram 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 spatiotemporal 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 has strong connections with data streams processing. This is a big challenge in the monitoring research area where the model serves as a reference for the diagnosis task.

The last point we consider is the decision part itself, mainly having abilities to propose repair policies 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 concerns how to automatically build the recommending actions from simulation or recorded data.