Du jeu de Go au Havannah : variantes d'UCT et coups décisifs

National audienceLes algorithmes de type fouille d'arbre Monte-Carlo et UCT (upper confidence tree) ont révolutionné le jeu de Go par ordinateur depuis 2006/2007. Quelques applications, encore rares, ont montré la généralité de ces approches, en particulier quand l'espace d'actions est trop grand pour les autres techniques, et quand l'état est complètement observable. Dans ce papier, nous testons cette généralité, en expérimentant UCT dans un autre jeu, le Havannah. Ce jeu est connu spécialement difficile pour les ordinateurs. Nous montrons que cette approche donne de bons résultats tout comme pour le jeu de Go, même si on peut noter quelques différences et en particulier la notion de coup décisif, inexistante en Go

Why one must use reweighting in Estimation Of Distribution Algorithms

International audienceWe study the update of the distribution in Estimation of Distribution Algorithms, and show that a simple modification leads to unbiased estimates of the optimum. The simple modification (based on a proper reweighting of estimates) leads to a strongly improved behavior in front of premature convergence

Bias and variance in continuous EDA

International audienceEstimation of Distribution Algorithms are based on statistical estimates. We show that when combining classical tools from statistics, namely bias/variance decomposition, reweighting and quasi-randomization, we can strongly improve the convergence rate. All modifications are easy, compliant with most algorithms, and experimentally very efficient in particular in the parallel case (large offsprings)

A new selection ratio for large population sizes

International audienceMotivated by parallel optimization, we study the Self-Adaptation algorithm for large population sizes. We first show that the current version of this algorithm does not reach the theoretical bounds, then we propose a very simple modification, in the selection part of the evolution process. We show that this simple modification leads to big improvement of the speed-up when the population size is large

A new selection ratio for large population sizes

International audienceMotivated by parallel optimization, we study the Self-Adaptation algorithm for large population sizes. We first show that the current version of this algorithm does not reach the theoretical bounds, then we propose a very simple modification, in the selection part of the evolution process. We show that this simple modification leads to big improvement of the speed-up when the population size is large

Learning opening books in partially observable games: using random seeds in Phantom Go

International audienceMany artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is far from being negligible. Then, we apply two different existing algorithms for selecting good seeds and good probability distributions over seeds. This mainly leads to learning an opening book. We apply this to Phantom Go, which, as all phantom games, is hard for opening book learning. We improve the winning rate from 50% to 70% in 5x5 against the same AI, and from approximately 0% to 40% in 5x5, 7x7 and 9x9 against a stronger (learning) opponent

Optimization of the Nested Monte-Carlo Algorithm on the Traveling Salesman Problem with Time Windows

International audienceThe traveling salesman problem with time windows is known to be a really difficult benchmark for optimization algorithms. In this paper, we are interested in the minimization of the travel cost. To solve this problem, we propose to use the nested Monte-Carlo algorithm combined with a Self-Adaptation Evolution Strategy. We compare the efficiency of several fitness functions. We show that with our technique we can reach the state of the art solutions for a lot of problems in a short period of time

Fully Parallel Hyperparameter Search: Reshaped Space-Filling

Space-filling designs such as scrambled-Hammersley, Latin Hypercube Sampling and Jittered Sampling have been proposed for fully parallel hyperparameter search, and were shown to be more effective than random or grid search. In this paper, we show that these designs only improve over random search by a constant factor. In contrast, we introduce a new approach based on reshaping the search distribution, which leads to substantial gains over random search, both theoretically and empirically. We propose two flavors of reshaping. First, when the distribution of the optimum is some known $P_0$, we propose Recentering, which uses as search distribution a modified version of $P_0$ tightened closer to the center of the domain, in a dimension-dependent and budget-dependent manner. Second, we show that in a wide range of experiments with $P_0$ unknown, using a proposed Cauchy transformation, which simultaneously has a heavier tail (for unbounded hyperparameters) and is closer to the boundaries (for bounded hyperparameters), leads to improved performances. Besides artificial experiments and simple real world tests on clustering or Salmon mappings, we check our proposed methods on expensive artificial intelligence tasks such as attend/infer/repeat, video next frame segmentation forecasting and progressive generative adversarial networks