Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go

International audienceThis paper presents a successful application of parallel (grid) coevolution applied to the building of an opening book (OB) in 9x9 Go. Known sayings around the game of Go are refound by the algorithm, and the resulting program was also able to credibly comment openings in professional games of 9x9 Go. Interestingly, beyond the application to the game of Go, our algorithm can be seen as a ”meta”-level for the UCT-algorithm: ”UCT applied to UCT” (instead of ”UCT applied to a random player” as usual), in order to build an OB. It is generic and could be applied as well for analyzing a given situation of a Markov Decision Process

Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo

National audienceNous combinons pour de l'exploration Monte-Carlo d'arbres de l'apprentissage arti- RÉSUMÉ. ficiel à 4 échelles de temps : – regret en ligne, via l'utilisation d'algorithmes de bandit et d'estimateurs Monte-Carlo ; – de l'apprentissage transient, via l'utilisation d'estimateur rapide de Q-fonction (RAVE, pour Rapid Action Value Estimate) qui sont appris en ligne et utilisés pour accélérer l'explora- tion mais sont ensuite peu à peu laissés de côté à mesure que des informations plus fines sont disponibles ; – apprentissage hors-ligne, par fouille de données de jeux ; – utilisation de connaissances expertes comme information a priori. L'algorithme obtenu est plus fort que chaque élément séparément. Nous mettons en évidence par ailleurs un dilemne exploration-exploitation dans l'exploration Monte-Carlo d'arbres et obtenons une très forte amélioration par calage des paramètres correspondant. We combine for Monte-Carlo exploration machine learning at four different time ABSTRACT. scales: – online regret, through the use of bandit algorithms and Monte-Carlo estimates; – transient learning, through the use of rapid action value estimates (RAVE) which are learnt online and used for accelerating the exploration and are thereafter neglected; – offline learning, by data mining of datasets of games; – use of expert knowledge coming from the old ages as prior information

Grid coevolution for adaptive simulations; application to the building of opening books in the game of Go

International audienceThis paper presents a successful application of parallel (grid) coevolution applied to the building of an opening book (OB) in 9x9 Go. Known sayings around the game of Go are refound by the algorithm, and the resulting program was also able to credibly comment openings in professional games of 9x9 Go. Interestingly, beyond the application to the game of Go, our algorithm can be seen as a ”meta”-level for the UCT-algorithm: ”UCT applied to UCT” (instead of ”UCT applied to a random player” as usual), in order to build an OB. It is generic and could be applied as well for analyzing a given situation of a Markov Decision Process

Combiner connaissances expertes, hors-ligne, transientes et en ligne pour l'exploration Monte-Carlo

National audienceNous combinons pour de l'exploration Monte-Carlo d'arbres de l'apprentissage arti- RÉSUMÉ. ficiel à 4 échelles de temps : – regret en ligne, via l'utilisation d'algorithmes de bandit et d'estimateurs Monte-Carlo ; – de l'apprentissage transient, via l'utilisation d'estimateur rapide de Q-fonction (RAVE, pour Rapid Action Value Estimate) qui sont appris en ligne et utilisés pour accélérer l'explora- tion mais sont ensuite peu à peu laissés de côté à mesure que des informations plus fines sont disponibles ; – apprentissage hors-ligne, par fouille de données de jeux ; – utilisation de connaissances expertes comme information a priori. L'algorithme obtenu est plus fort que chaque élément séparément. Nous mettons en évidence par ailleurs un dilemne exploration-exploitation dans l'exploration Monte-Carlo d'arbres et obtenons une très forte amélioration par calage des paramètres correspondant. We combine for Monte-Carlo exploration machine learning at four different time ABSTRACT. scales: – online regret, through the use of bandit algorithms and Monte-Carlo estimates; – transient learning, through the use of rapid action value estimates (RAVE) which are learnt online and used for accelerating the exploration and are thereafter neglected; – offline learning, by data mining of datasets of games; – use of expert knowledge coming from the old ages as prior information

CC9 Livestock-Associated Staphylococcus aureus Emerges in Bloodstream Infections in French Patients Unconnected With Animal Farming

We report 4 bloodstream infections associated with CC9 agr type II Staphylococcus aureus in individuals without animal exposure. We demonstrate, by microarray analysis, the presence of egc cluster, fnbA, cap operon, lukS, set2, set12, splE, splD, sak, epiD, and can, genomic features associated with a high virulence potential in human

Effects of Cannabinoids on Caffeine Contractures in Slow and Fast Skeletal Muscle Fibers of the Frog

The effect of cannabinoids on caffeine contractures was investigated in slow and fast skeletal muscle fibers using isometric tension recording. In slow muscle fibers, WIN 55,212-2 (10 and 5 μM) caused a decrease in tension. These doses reduced maximum tension to 67.43 ± 8.07% (P = 0.02, n = 5) and 79.4 ± 14.11% (P = 0.007, n = 5) compared to control, respectively. Tension-time integral was reduced to 58.37 ± 7.17% and 75.10 ± 3.60% (P = 0.002, n = 5), respectively. Using the CB1 cannabinoid receptor agonist ACPA (1 μM) reduced the maximum tension of caffeine contractures by 68.70 ± 11.63% (P = 0.01, n = 5); tension-time integral was reduced by 66.82 ± 6.89% (P = 0.02, n = 5) compared to controls. When the CB1 receptor antagonist AM281 was coapplied with ACPA, it reversed the effect of ACPA on caffeine-evoked tension. In slow and fast muscle fibers incubated with the pertussis toxin, ACPA had no effect on tension evoked by caffeine. In fast muscle fibers, ACPA (1 μM) also decreased tension; the maximum tension was reduced by 56.48 ± 3.4% (P = 0.001, n = 4), and tension-time integral was reduced by 57.81 ± 2.6% (P = 0.006, n = 4). This ACPA effect was not statistically significant with respect to the reduction in tension in slow muscle fibers. Moreover, we detected the presence of mRNA for the cannabinoid CB1 receptor on fast and slow skeletal muscle fibers, which was significantly higher in fast compared to slow muscle fiber expression. In conclusion, our results suggest that in the slow and fast muscle fibers of the frog cannabinoids diminish caffeine-evoked tension through a receptor-mediated mechanism