Olivier Teytaud

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We show some mathematical links between partially observable (PO) games in which information is regularly revealed, and simultaneous actions games. Using this, we study the extension of Monte-Carlo Tree Search algorithms to PO games and to games with simultaneous actions. We apply the results to Urban Rivals, a free PO internet card game with more than 10(More)
The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods.(More)
The search for interesting Boolean association rules is an important topic in knowledge discovery in databases. The set of admissible rules for the selected support and con dence thresholds can easily be extracted by algorithms based on support and con dence, such as Apriori. However, they may produce a large number of rules, many of them are uninteresting.(More)
The Monte-Carlo Tree Search algorithm has been successfully applied in various domains. However, its performance heavily depends on the Monte-Carlo part. In this paper, we propose a generic way of improving the Monte-Carlo simulations by using RAVE values, which already strongly improved the tree part of the algorithm. We prove the generality and efficiency(More)
In order to promote computer Go and stimulate further development and research in the field, the event activities, Computational Intelligence Forum and World 9times9 Computer Go Championship, were held in Taiwan. This study focuses on the invited games played in the tournament Taiwanese Go players versus the computer program MoGo held at the National(More)
Upper Confidence Trees are a very efficient tool for solving Markov Decision Processes; originating in difficult games like the game of Go, it is in particular surprisingly efficient in high dimensional problems. It is known that it can be adapted to continuous domains in some cases (in particular continuous action spaces). We here present an extension of(More)
We present a new exploration term, more efficient than classical UCT-like exploration terms and combining efficiently expert rules, patterns extracted from datasets, All-Moves-As-First values and classical online values. As this improved bandit formula does not solve several important situations (semeais, nakade) in computer Go, we present three other(More)