From: Associate Professor at University of Paris-Saclay (France)
Time and date: 4pm, 12th December 2017
Room: Eng3.24, Engineering Building, QMUL Mile End campus (building 15 on the campus map)
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As usual, refreshments will be served before and after the seminar in the hub. Please [register](https://www.eventbrite.co.uk/e/bridge-a-new-challenge-for-ai-tickets-39115905727) for helping preparing the refreshments.
## Abstract
Games have always been an excellent field of experimentation for the nascent techniques in computer science and in different areas of Artificial Intelligence (AI) including Machine Learning (ML). Despite their complexity, game problems are much easier to understand and to model than real life problems. Systems initially designed for games are then used in the context of real applications. In the last decades, designs of champion-level systems dedicated to a game (game AI) were considered as milestones of computer science and AI.
Go and Poker are the two most recent successes. In May 2017, AlphaGo (DeepMind) defeated by 3 to 0 the Go world champion Ke Jie. In January 2017, the Poker AI Libratus (Carnegie Mellon University) won a heads-up no-limit Texas hold’em poker event against four of the best professional players.
This success has not yet happened with regard to another incomplete information cards game, namely Bridge, which then provides a challenging problem for AI.
We think that Deep Learning (DL) cannot be the only AI future. There are many Machine Learning and more generally AI fields which can interact with DL. Bridge is a great example of an application needing more than black box approaches. The AlphaBridge project is dedicated to the design of a Bridge AI taking up this challenge by using hybrid framework in the field of Artificial Intelligence.
The first part of the webinar is devoted to the presentation of the different aspects of bridge and of various challenges inherent to it. In a second part, we will present our work concerning the optimization of the AI Wbridge5 developed by Yves Costel. This work is based on a recent seed methodology (T. Cazenave, J. Liu and O. Teytaud 2015, 2016) which optimizes the quality of Monte-Carlo simulations and which has been defined and validated in other games. The Wbridge5 version boosted with this method won the World Computer-Bridge Championship twice, in September 2016 and in August 2017. Finally, the last part is about various ongoing works related to the design of a hybrid architecture entirely dedicated to bridge using recent numeric and symbolic Machine Learning modules.
## Bio
PhD in Artificial Intelligence (Knowledge Representation and Machine Learning) in 1997.
Associate professor at University Paris Saclay, France since 1998. Before joining in 2015 the group A&O in the interplay of Machine Learning and Optimization, she worked in the group LaHDAK (Large-scale Heterogeneous DAta and Knowledge) at Laboratory of Computer Science (LRI).
She started playing bridge in 2004 and is now 59th French woman player out of 48644 players.
In 2015, she set up the AlphaBridge project combining her two passions. AlphaBridge is dedicated to solve the game of bridge by defining a hybrid architecture including recent numeric and symbolic Machine Learning modules.
## Useful links
* If you don’t know Bridge and want to know how to play it: [http://www.learn2playbridge.com/](http://www.learn2playbridge.com/)
* If you want to play Bridge online: [https://www.bridgebase.com/](https://www.bridgebase.com/)