Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks. A signature aim of our group is to use the resources and expertise made available to us at DeepMind in deep reinforcement learning to explore multi-agent systems in complex environments and use these benchmarks to advance our understanding. Here, we summarise the recent work of our team and present a taxonomy that we feel highlights many important open challenges in multi-agent research.Comment: Published in AI Communications 202

Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players' and coordinated teams' behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

Container 3 : de puntzak van Heine

Transscriptie van de uitzending (3 mei 1989) van het programma Container over 'De puntzak van Heine', een collage van teksten en beelden over onrust en omwenteling. Container was een rechtstreeks uitgezonden televisieprogramma op BRT van Jef Cornelis, Lieven De Cauter en Bart Verschaffel. De transscriptie past in een onderzoeksproject van Koen Brams en Dirk Pültau over het oeuvre van de televisiemaker en cineast Jef Cornelis

The application of 'Big Data Analysis' techniques in GSR research: a novel approach to an ever evolving forensic discipline

Although the use of SEM/EDX equipment for GunShot Residue (GSR) analysis was already introduced in the 1980's and has since been used for decades in the search for microscopic primer particles, the technique has continuously evolved. Part of the drive for this ongoing development comes from the continuous changes in the composition of ammunition primers. Recently, munition manufacturers are progressing away from the 'classic' compositions, containing (heavy) metals, with the introduction of primers containing no metallic elements. Especially this recent innovation poses severe problems for modern analysis systems. The commercial GSR analysis software depends on the BackScattered Electron signal of the metal GSR particles to set them apart from the Environmental Particles (EP), which are present in abundance on any sampler. However, as the mean Z of these metal-free GSR particles will approach that of the EP, the standard procedures and the parameter settings of these search algorithms will probably fail. Although as a partial solution other signals could be used for the detection of the relevant particles, such as Secondary Electrons or Cathode Luminescence, a much larger number of potential GSR particles will have to be analysed because a number of EP will also be selected as potential GSR particles. Finally, the EDX classification algorithms may encounter problems in discerning GSR particles from EP because of their similar chemical composition. The use of Big Data Analysis (BDA) techniques is a novel approach in the GSR field, which may yield a solution for a number of the problems posed by these new primers. In order to implement these BDA techniques, a database of the GSR particles is compiled, together with databases of EP. Against these 'Ground Truth' databases, a test sample's particle populations can be compared as a group, which will potentially yield a shortlist of munition types which produce similar particle groups. In order to develop and test these techniques, databases were compiled using classic munition data which was readily available from case samples. In this presentation, the preliminary results of this study, which involves researchers from three Belgian universities, all working within the iMinds ICON BAHAMAS project (2015-2016), will be discussed

Inside the Visible : An Elliptical Traverse of 20th Century Art in, of, and from the Feminine

This exhibition catalogue, edited by the curator, De Zheger, surveys the contribution of women to 20th century art through the work of 37 artists, splitting the contents into four thematic sections, each of which are divided into three periods: the 1930s-40s, the 1960s-70s, and the 1990s. The four sections address, respectively, feminine plurality and difference, the poetics of alterity, the interweaving of language and space, and finally, materiality and embodiment. Index, 4 p. List of works. Biographical notes. Bibliography 8 p. Circa 660 bibl. ref