Ms Pac-Man versus Ghost Team CEC 2011 competition

Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE

Simple atomic quantum memory suitable for semiconductor quantum dot single photons

Quantum memories matched to single photon sources will form an important cornerstone of future quantum network technology. We demonstrate such a memory in warm Rb vapor with on-demand storage and retrieval, based on electromagnetically induced transparency. With an acceptance bandwidth of $\delta f$ = 0.66~GHz the memory is suitable for single photons emitted by semiconductor quantum dots. In this regime, vapor cell memories offer an excellent compromise between storage efficiency, storage time, noise level, and experimental complexity, and atomic collisions have negligible influence on the optical coherences. Operation of the memory is demonstrated using attenuated laser pulses on the single photon level. For 50 ns storage time we measure $\eta_{\textrm{e2e}}^{\textrm{50ns}} = 3.4(3)\%$ \emph{end-to-end efficiency} of the fiber-coupled memory, with an \emph{total intrinsic efficiency} $\eta_{\textrm{int}} = 17(3)\%$. Straightforward technological improvements can boost the end-to-end-efficiency to $\eta_{\textrm{e2e}} \approx 35\%$; beyond that increasing the optical depth and exploiting the Zeeman substructure of the atoms will allow such a memory to approach near unity efficiency. In the present memory, the unconditional readout noise level of $9\cdot 10^{-3}$ photons is dominated by atomic fluorescence, and for input pulses containing on average $\mu_{1}=0.27(4)$ photons the signal to noise level would be unity

Lumen: A software for the interactive visualization of probabilistic models together with data

Research in machine learning and applied statistics has led to the development of a plethora of different types of models. Lumen aims to make a particular yet broad class of models, namely, probabilistic models, more easily accessible to humans. Lumen does so by providing an interactive web application for the visual exploration, comparison, and validation of probabilistic models together with underlying data. As the main feature of Lumen a user can rapidly and incrementally build flexible and potentially complex interactive visualizations of both the probabilistic model and the data that the model was trained on. Many classic machine learning methods learn models that predict the value of some target variable(s) given the value of some input variable(s). Probabilistic models go beyond this point estimation by predicting instead of a particular value a probability distribution over the target variable(s). This allows, for instance, to estimate the prediction’s uncertainty, a highly relevant quantity. For a demonstrative example consider a model predicts that an image of a suspicious skin area does not show a malignant tumor. Here it would be extremely valuable to additionally know whether the model is sure to 99.99% or just 51%, that is, to know the uncertainty in the model’s prediction. Lumen is build on top of the modelbase back-end, which provides a SQL-like interface for querying models and its data (Lucas, 2020)

Signature of frustrated moments in quantum critical CePd1−x_{1-x}Nix_xAl

CePdAl with Ce $4f$ moments forming a distorted kagom\'e network is one of the scarce materials exhibiting Kondo physics and magnetic frustration simultaneously. As a result, antiferromagnetic (AF) order setting in at $T_{\mathrm{N}} = 2.7$~K encompasses only two thirds of the Ce moments. We report measurements of the specific heat, $C$, and the magnetic Gr\"uneisen parameter, $\Gamma_{\rm mag}$, on single crystals of CePd$_{1-x}$Ni$_x$Al with $x\leq 0.16$ at temperatures down to 0.05~K and magnetic fields $B$ up to $8$~T. Field-induced quantum criticality for various concentrations is observed with the critical field decreasing to zero at $x_c\approx 0.15$. Remarkably, two-dimensional (2D) AF quantum criticality of Hertz-Millis-Moriya type arises for $x=0.05$ and $x=0.1$ at the suppression of 3D magnetic order. Furthermore, $\Gamma_{\rm mag}(B)$ shows an additional contribution near $2.5$~T for all concentrations which is ascribed to correlations of the frustrated one third of Ce moments.Comment: 5+2 pages with 4+3 figure

CGiS : high-level data-parallel GPU programming

In the last few years, PC technology underwent a paradigm shift. The current trend leads aways from raising sequential performance to enhancing the available parallelism. The rapid performance increase of Graphics Processing Units (GPUs) is a part of this trend. However, it is difficult to harness the computational potential because for the longest time GPUs could be directed only through graphics APIs and in low-level code. The language CGiS has been developed to remedy this situation. CGiS is a data-parallel programming language, which offers a high-level abstraction of GPUs, letting programmers use GPUs as co-processors for massively parallel algorithms. This work presents the language and the compiler for CGiS in the context of general purpose programming on GPUs (GPGPU).Seit einigen Jahren zeichnet sich bei handelsüblichen PCs ein Trend weg von der Erhöhung der sequentiellen Leistung hin zur Parallelverarbeitung ab. Ein Bestandteil dieses Trends ist die rasche Leistungsentwicklung der Grafikkarten (GPUs), deren Rechenleistung die aktueller CPUs mittlerweile übertrifft. Es ist jedoch schwierig, diese Leistung auch abzurufen, da diese Geräte lange Zeit nur hardwarenah und über Grafik-APIs ansteuerbar waren. Um dies zu ändern, ist CGiS entwickelt worden, eine datenparallele Programmiersprache, die die GPUs abstrahiert und ihre Benutzung als Co-Prozessoren für massiv-datenparallele Algorithmen ermöglicht. Diese Arbeit stellt die Sprache und den Compiler im Kontext dieser Entwicklung vor

An artificial Rb atom in a semiconductor with lifetime-limited linewidth

We report results important for the creation of a best-of-both-worlds quantum hybrid system consisting of a solid-state source of single photons and an atomic ensemble as quantum memory. We generate single photons from a GaAs quantum dot (QD) frequency-matched to the Rb D2-transitions and then use the Rb transitions to analyze spectrally the quantum dot photons. We demonstrate lifetime-limited QD linewidths (1.48 GHz) with both resonant and non-resonant excitation. The QD resonance fluorescence in the low power regime is dominated by Rayleigh scattering, a route to match quantum dot and Rb atom linewidths and to shape the temporal wave packet of the QD photons. Noise in the solid-state environment is relatively benign: there is a blinking of the resonance fluorescence at MHz rates but negligible upper state dephasing of the QD transition. We therefore establish a close-to-ideal solid-state source of single photons at a key wavelength for quantum technologies

Predicting Dominance Rankings for Score-Based Games

Game competitions may involve different player roles and be score-based rather than win/loss based. This raises the issue of how best to draw opponents for matches in ongoing competitions, and how best to rank the players in each role. An example is the Ms Pac-Man versus Ghosts Competition which requires competitors to develop software controllers to take charge of the game's protagonists: participants may develop software controllers for either or both Ms Pac-Man and the team of four ghosts. In this paper, we compare two ranking schemes for win-loss games, Bayes Elo and Glicko. We convert the game into one of win-loss ("dominance") by matching controllers of identical type against the same opponent in a series of pair-wise comparisons. This implicitly creates a "solution concept" as to what a constitutes a good player. We analyze how many games are needed under two popular ranking algorithms, Glicko and Bayes Elo, before one can infer the strength of the players, according to our proposed solution concept, without performing an exhaustive evaluation. We show that Glicko should be the method of choice for online score-based game competitions