Section: Overall Objectives
In 2008, TAO aimed at modelling, predicting, and ultimately controllling physical or artificial systems through seamless integration of statistical Machine Learning (ML) and Optimization approaches. Systems under study range from large-scale engineering systems to physical or chemical phenomenons, including robotics and games, with emphasis on incomplete information, dynamic environments, and ill-posed optimization problems.
One major breakthrough of TAO regarding optimal control in uncertain environments was illustrated by the MoGo program, first ever Computer-Go program to win a professional human player (more in section 6.5 ). This achievement, acknowledged a “notable success of AI” by The Economist (jan. 2007) is based on original tree-structured extensions of Multi-Armed Bandit algorithms (Fig. 1). Its parallelization was undertaken in collaboration with the Parall team and Bull; collaborations with Microsoft started in 2008. Notably, the approach involves little specific domain knowledge; it directly tackles optimal policy learning under uncertain/incomplete information. This work has been widely disseminated to the general public: IA-Go Challenge in Paris in March; Gold medal Olympiads 2008.
Other fundamental results regard the adaptation of statistical learning theory tools to evolutionary optimization. Specifically, Monte Carlo Markov Chains were used for proving the convergence of Evolution Strategies (ES) and analysing their convergence rate. Other results regard the optimization of computationally expensive functions or the use of quasi-random algorithms. The robustness of ES compared to standard, Newton-based approaches in convex settings with high condition number has been demonstrated experimentally.
EC-based applications in Numerical Engineering require the design of representations enforcing stability, compacity, and versatility; various search spaces have been considered, ranging from developmental representations to Echo State Networks. Architectural applications in collaboration with EZCT, opened promising avenues for research (exhibitions at the Beaubourg Museum of Modern Art; primed contribution to the Seroussi contest, Fig. 2).
TAO also actively participated in the pioneering of ML-based Autonomic Computing, a major field of applications for both learning and optimization. This research theme benefitted from the arrival and expertise of Balász Kégl in 2007, strengthening the link between LRI and Laboratoire de l'Accélérateur Linéaire. In collaboration with the Enabling Grid for E-SciencE project (EGEE, FP6 and FP7, 2004-2010), a Grid Observatory ( http://www.grid-observatory.org/ ) acted to gather and publish traces of grid activity, permitting the behavioural modelling of the grid with its self-management as ultimate goal. Independently, active relational learning has been applied to the generation of test cases for structural statistical software testing (coll. ForTeSe, LRI). Finally, a joint initiative (Microsoft-INRIA) is concerned with automatic parameter tuning.
During the year 2008, TAO has confirmed its position as a key research group in Evolutionary Computation (invited talks at GPTP'08, IEEE CIS'2008, 8 thISEA in June 2009; editor in chief of MIT Press Evolutionary Computation journal) and in Machine Learning (steering committee of PASCAL and PASCAL-2 NoEs, 2003-2013). It has expanded its activities to Data Mining (KDUbiq CA; invited talk at IEEE Forum on DM) and Complex Systems (chair of ANR SYSCOMM Evaluation Committee). TAO activities have also been recognized as a source of applicative breakthroughs (OMD RNTL; MoGo; coll. with EZCT nominated for Seroussi contest). Following earlier recommendations, TAO 's robotics activities are now supported by an active collaboration with hardware roboticists (SYMBRION IP).