Section: Research Program

Simulator-based decision support systems

A common way to investigate and understand complex phenomena, such as those related to ecosystems, consists in designing a computational model and implementing a simulator to test the system behavior under various parameters. Analyzing the ouput data of such simulators enables a fine grained understanding of the system studied, however huge quantities of data are produced. To be able to exploit these simulators in decision support scenarios, it is thus critical to provide methods to simplify the interactions with the simulator and handle the large quantity of data produced.

• One approach is to store all the simulation data in a datawarehouse and provide scientists and experts with tools to analyze efficiently the simulation data. Providing users with means to dig through large amount of multidimensional data, from more or less abstract viewpoints, and express preferences on the returned results is an important research topic in databases and data mining. To this end, Skyline queries constitute a relevant approach as they retrieve the most interesting objects with respect to multi-dimensional criteria with the possibility of making compromises on conflicting dimensions. The challenge is to define and implement skyline queries in a datawarehouse context. In this field, we are investigating efficient interactive tools for answering dynamic [51] and hierarchical [52] skyline queries.

• Another approach is to simplify the simulation model. For some applications, the system is too complex for a traditional numerical simulation to give relevant results in a short amount of time. It is especially the case when data and knowledge are not available to supply numerical models. Qualitative models offer a good alternative to model complex systems in this context. This abstracted representation offers an efficient computation on model exploration and gives relevant results when querying the system behavior. In the Dream project-team we are focusing on qualitative models of dynamical systems described as Discrete Event Systems (DES). Recent studies have emphasized the great interest of coupling model-checking techniques with qualitative models. We propose to use the timed automata formalism that allow the explicit representation of time [45] . In this context, the research issues we investigate are the following.

  • The size of a global model constructed from an abstracted description of the system and domain knowledge is potentially huge. A challenging problem is to reduce the size of this model using artificial intelligence tools [54] .

  • It is necessary to propose a high-level language to explore and predict future changes of the system. Using this language, a stakeholder should express easily any requirements he wants to ask on the system behavior. We investigate the formalization of query patterns relying on recent temporal logics that can be exploited using model-checking techniques [68] .

  • Simulators are good at providing ouput data for a fixed time period. Another challenge is to determine if some state can be reached (reachability problem) and keep on simulate until reaching this state. A further challenge is computating the optimal strategy for a reachability problem ("what is the best sequence of actions to reach a specific state at a specific time ?"). In this case we propose to use extended timed automata, such as timed game automata or priced time automata, with controller synthesis methods [46] .

• When modelling becomes increasingly complex because of ever-increasing numbers of combined processes, making model-based decision aids are essential. Our approach uses symbolic learning techniques on simulated data to synthetise complex processses and help in decision making. Thus, rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward, especially when their number is high.

  Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted to an "unsatisfactory situation" where his expectations or hypotheses are not met. In this case, he needs help to decide about the appropriate action to remedy to this situation. The method consists in comparing the situation to a set of classification rules. For this purpose, we have proposed a framework for learning action recommendations dealing with complex notions of feasibility and quality of actions [80] .