Christophe Fiter

Bio: Christophe Fiter is an academic researcher from university of lille. The author has contributed to research in topics: Sampling (statistics) & Exponential stability. The author has an hindex of 9, co-authored 41 publications receiving 540 citations. Previous affiliations of Christophe Fiter include Centre national de la recherche scientifique & Tel Aviv University.

Adding expert knowledge and exploration in monte-carlo tree search

Abstract: We present a new exploration term, more efficient than classical UCT-like exploration terms. It combines 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 important improvements which are central in the recent progress of our program MoGo. We show an expert-based improvement of Monte-Carlo simulations for nakade situations; we also emphasize some limitations of this modification. We show a technique which preserves diversity in the Monte-Carlo simulation, which greatly improves the results in 19x19. Whereas the UCB-based exploration term is not efficient in MoGo, we show a new exploration term which is highly efficient in MoGo. MoGo recently won a game with handicap 7 against a 9Dan Pro player, Zhou JunXun, winner of the LG Cup 2007, and a game with handicap 6 against a 1Dan pro player, Li-Chen Chien.

Stabilization of switched affine systems with disturbed state-dependent switching laws

Abstract: In this paper we investigate the stabilization problem for a class of switched affine systems with a state-dependent switching law. Since the states measurements are in general subject to perturbations and noises, we propose a robust switching law design method. Qualitative conditions for the stability of the closed-loop switched system are given. Stability conditions are also formulated as Linear Matrix Inequalities (LMIs) in order to allow numerical implementations. Results are illustrated by numerical examples in order to show the efficiency of the method and its limits.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A Survey of Monte Carlo Tree Search Methods

Abstract: Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

Abstract: This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, $H_\infty$ filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.