Section: Overall Objectives
Context and overall goal of the project
The overall goals of the project are to model, to predict, to understand, and to control physical or artificial systems. The central claim is that Learning and Optimisation approaches must be used, adapted and integrated in a seamless framework, in order to bridge the gap between the system under study on the one hand, and the expert's goal as to the ideal state/functionality of the system on the other hand.
Specifically, our research context involves the following assumptions:
The systems under study range from large-scale engineering systems to physical or chemical phenomenons, including games. Such systems, sometimes referred to as complex systems , can hardly be modelled based on first principles due to their size, their heterogeneity and the incomplete information aspects involved in their behaviour.
Such systems can be observed; indeed selecting the relevant observations and providing a reasonably appropriate description thereof is part of the problem to be solved. A further assumption is that these observations are sufficient to build a reasonably accurate model of the system under study.
The available expertise is sufficient to assess the system state, and any modification thereof, with respect to the desired states/functionalities. The assessment function is usually not a well-behaved function (differentiable, convex, defined on a continuous domain, etc), barring the use of standard optimisation approaches and making Evolutionary Computation a better suited alternative.
In this context, the objectives of TAO are threefold:
using Evolutionary Computation (EC) and more generally Stochastic Optimisation to support Machine Learning (ML);
using Statistical Machine Learning to support Evolutionary Computation;
investigating integrated ML/EC approaches on diversified and real-world applications.
Due to the unavoidable shift of the scientific environement and people interest after 4 years of activity, the detailed implementation of those objectives have been slightly revised since the initial project proposal 4 years ago, and updated lines of research will be described in next section 3.1 .