LIP6: comment y aller
Abstract: Today, there are many graphical formalisms for modelling software -- among others the Unified Modelling Language (UML). And there are many technologies that make use of these models to automatically generate code from these models. The focus of today's tools, however, is still on the structural models; the behaviour still needs to be programmed by hand. In some specific application areas, however, there are new technologies that allow us to make software without programming a single line of code. One example of such a technology is the Graphical Modelling Framework (GMF), which is part of the Eclipse Technology. GMF allows us to implement graphical editors without doing any programming. Another field of application is workflow management, which had this idea of modelling software 10 years ahead of time. The talk will give a glimpse of how we could develop software in 10 years -- without doing any programming at all. It will discuss the necessary theory and technologies for achieving that, and it will show that the pieces for achieving this vision are already there; we only need to put them together.
Abstract: we propose a new approach for the exploration of the parameter space of agent-based models: Adaptative Dichotomic Optimizati:w on. Agent-based models are generally characterized by a great number of parameters, a lot of which cannot be evaluated with the current knowledge about the real system. The aim of the work is to provide tools for the calibration of these models, which consists in finding the optimal set of parameters for a given criterion. The criterion can be for example that the model achieves a specific function optimally or that the results of the simulation are as close to possible of experimental data. Our approach is based on the partition of the parameter space (the interval of variation of each variable is divided into a finite number of intervals) and on a parallel exploration of the various parameters by the agents of the model. The navigation in the parameter space is done by grouping or dividing adaptively some of the intervals, according to an algorithm which is adapted from Ant Colony Systems.