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52: Machine Learning at the UTC-Heudiasyc lab

Professor Philippe Bonnifait, also Vice-Chairman of the scientific council of the University of Technology of Compiègne (UTC) former Director of a CNRS research group (GDR) in robotics between 2013 and 2017. Since January 2018, he has been Director of the UTC-Heudiasyc Laboratory, created in 1981. This state-of-the-art laboratory houses, in particular, the CID (Knowledge, Uncertainties, Data) team dedicated to research in artificial intelligence.

52: Machine Learning at the UTC-Heudiasyc lab

Is artificial intelligence a new form of magic?

Professor, Sylvain Lagrue was appointed to UTC-Compiegne in September 2018. In his capacity as research scientist in the Knowledge, Uncertainties, Data (CID) team at Heudiasyc, a joint UTC/CNRS unit, he is working on the logical representation of knowledge and reasoning, the management of uncertainty in artificial intelligence, and decision making and games.

After his DEA (currently the Master 2 diploma) in Artificial Intelligence (AI), Sylvain Lagrue has been working on a European project on "Taking uncertainties into account for preference modelling in geographic information systems". After completing his thesis, he joined the University of Artois in 2004 as a lecturer before joining the UTC as a full professor.

His role within the CID team? "My crossdisciplinary profile allows me to work with the different researchers in the team. Both in the field of "the uncertain" and that of "knowledge representation", for example," he says.

And how does AI fit concretely into all this? "For the public at large, AI is magic made by the computer. And the more magical it is, the more AI. In other words, seeing actions made by computers that we thought were impossible," he says.

One example among others? "Let's consider games. When IBM's Deep Blue defeated world chess champion Kasparov in 1997, the general public thought that AI was going to take everything in its path, and then it calmed down. The reason? It was noticed, after analysis, that what won in 1997 was the computer's computing capacity. For the general public, this is no longer magic. So it's no longer AI," he explains.

But then what is AI in his mind? "It's about making a machine reason when you don't expect it to be able to do it. So there's a whole aspect of logic, but also of decision making. In a word, making it reason and make intelligent choices," he describes.

This is reflected in its three areas of research. The logical representation of knowledge and reasoning? "Logic has always - since ancient times - been a way to formalize reasoning based on a certain number of hypotheses allowing us to draw conclusions that are valid. Our objective is to see this type of advanced reasoning done by a machine. This can be achieved efficiently thanks to resolution and deduction algorithms which, based on the hypotheses, ultimately allow a machine to make decisions", stresses Sylvain Lagrue.

A skill that has led him to work on a European project aimed at "safeguarding intangible heritage in South-East Asia and in particular the Water Puppets of Vietnam, whose playlets represent the country's history, legends, scenes from daily life, etc. "All this is accompanied by music, songs and recitations. In terms of richness, they can be compared to opera in Europe. So we had to represent a lot of complex knowledge," he says.

Managing uncertainty with AI? "If you roll a die, you don't know which side it's going to fall on. However, in this case, we do have probabilities. In other cases, we don't even have probabilities. In the formalisms that I use, the challenge is to model a sequence of "we think that such and such an action leads to this but in the opposite case rather to that". In short, a much more ordinal modelling," he says.

Finally, is there an interest in AI games? "The advantage of gaming? It allows us to have a controlled universe. You know what environment you're in, with its precise rules, whose effects you know, and you don't have to worry about the physical aspects. It allows us to test a large number of algorithms," he explains.

An interest that led him to co-direct a thesis on "general game playing", or how to make a computer play any game. "Deep Blue could only play chess, for example. In order to develop a program capable of playing all games, we had to represent all games with complete information thanks to the Game Description Language (GDL). Which brings us back again to the representation of knowledge", concludes Sylvain Lagrue.