Team tao

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Section: Overall Objectives

Highlights

Figure 1. Tree-structured Multi-armed Bandits, the MoGo algorithm
IMG/arbreGo

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 to be 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, and a branch of MoGo has been developped jointly with the National University of Tainan (NUTN, Taiwan). Notably, the approach involves little specific domain knowledge; it directly tackles optimal policy learning under uncertain/incomplete information.

Recent developments of MoGo include the use of patterns (off-line learning) and the on-line exploitation of information gathered through the search, referred to as RAVE. The combined use of patterns and RAVE pioneered by TAO is now widely used in the litterature. Among the applications of the general MoGo principles are the optimization of the mathematical Spiral library (coll. CMU).

This work has been widely disseminated to the general public: IA-Go Challenge in Paris in March; Gold medal Olympiads 2008, first ever win against a pro in 9x9 Go in 2007, first ever win against a pro in 9x9 Go in 2008 without blitz, first ever win against a pro in 19x19 Go with handicap 9 in 2008. Three more world successes (still at the top of the state of the art as of Nov. 2009) have been featured by TAO in 2009, win against i) a top professional player with handicap 7 (Tainan 2009); ii) a professional player with handicap 6 (Tainan 2009); and iii) a top professional player as black in 9x9 Go(With the standard komi 7.5, 9x9 Go is more difficult for the black; this result is the first win ever in such a situation.) (Taipei 2009).

Other fundamental results regard:

During the year 2009, TAO has confirmed its position as a key research group in Evolutionary Computation (invited talks at 8th ISEA in June 2009, at GECCO 2009, at 3rd Workshop TRSH; Editor in Chief of MIT Press Evolutionary Computation journal; Best Paper Awards at LION'09 conference in January, at EvoBIO'09 in April, and in the Continuous Optimization track at ACM-GECCO in July; as well as in Machine Learning (steering committee of PASCAL and PASCAL-2 NoEs, 2003-2013; chair of ECML/PKDD 2010). It has expanded its activities to Data Mining (invited talk at IEEE Forum on DM 2008) and Complex Systems (chair of ANR SYSCOMM Evaluation Committee; invited talk at Eur. Conf. on Complex Systems 2009). 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), witnessed by the organization of two robotics events (collocated workshops at IROS and ECML/PKDD 2009).


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