Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
XML PDF e-pub
PDF e-Pub

Section: New Results

Simulator-based decision support

Participants : Louis Bonneau de Beaufort, Tassadit Bouadi, Marie-Odile Cordier, Thomas Guyet, Christine Largouët, Véronique Masson, René Quiniou, Sophie Robin, Laurence Rozé, Yulong Zhao.

Model-checking an ecosystem for decision-aid

In previous work we have proposed to use qualitative modelling to model ecosystems and we defined a set of high level query patterns to explore th system [53] . This approach has been applied on real-case ecosystems (coral-reef ecosystem in New-Caledonia, fisheries ecosystem in the English channel) and implemented in a tool called EcoMata.

In recent studies we have focussed on the formalization of the qualitative model automatically built from an abstracted ecosystem description. Ecosystems share some common features with concurrent systems represented in the model-checking field: the system complexity is due to interacting components and the system evolution is event-driven and submitted to temporal constraints. However if model-checking techniques are dedicated to finite state systems, ecosystem models are usually represented by analytical models as a set of differential equations. Some studies present how to quantize continuous-time systems in order to diagnose them as discrete-event systems. We proposed a method to build automatically a network of timed automata from various information on the system: description of interactions between components, human knowledge, simple models of population dynamics. The key point is to quantize the continuous-time sub-systems and to get a qualitative model described as network of timed automata. To reduce the size of this network, important after the automatic generation, a learning machine algorithm has been applied in order to reduce the number of "similar" locations. This work has been published in [37] .

Controler synthesis for optimal strategy search

Similarly to previous work, this approach relies on a qualitative model of a dynamical system. The problem consists in finding a strategy in order to help the user achieveing a specific goal. The model is now considered as a timed game automata expressing controllable and uncontrollable actions. The strategy represents the sequence of actions that can be performed by a user to reach a particular state (in case of a reachability problem for instance). A first approach based on a "generate and test" method has been developped for the marine ecosystem example [69] .

More recently, two new methods for finding the optimal strategies have been proposed. The first one uses controller synthesis on timed automata and exploits the efficency of well-recognized tools. The second one deals with a set of similar models and extracts a more general strategy, closer to what is expected by the stakeholders. These methods have been applied in the context of herd management on a catchment. Yulong Zhao defended his Phd this year on this research subject [5] .

A datawarehouse for simulation data

In previous work we have proposed a datawarehouse architecture to store the huge data produced by deep agricultural simulation models [35] . This year, we have worked on hierarchical skyline queries to introduce skyline queries in a datawarehouse framework. Conventional skyline queries retrieve the skyline points in a context of dimensions with a single hierarchical level. However, in some applications with multidimensional and hierarchical data structure (e.g. data warehouses), skyline points may be associated with dimensions having multiple hierarchical levels. Thus, we have proposed an efficient approach reproducing the effect of the OLAP operators "drill-down" and "roll-up" on the computation of skyline queries [10] , [25] . It provides the user with navigation operators along the dimensions hierarchies (i.e. specialize / generalize) while ensuring an online calculation of the associated skyline.

Post-mining classification rules

We consider sets of classification rules with quantitative attributes inferred by supervised machine learning, as in the framework of the Sacadeau project. Our aim is to improve human understanding of such sets of rules. Often, output quantitative rules contains too many intervals that are difficult to intepret. It is thus important to merge some of these intervals in order to get more understandable rules. However, blindly merging rules may decrease rule quality. To counter that, we proposed two algorithms for merging intervals via clustering techniques that take into account the final rule quality. The approach automatically detects the most adapted number of clusters required to merge intervals while maintaining rule quality.