While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often somewhat specialised and computationally inefficient. In this paper, we describe the "ludemic" general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes -- high-level, easily understandable game concepts -- which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.
While current General Game Playing (GGP) systems facilitate useful research in Artificial Intelligence (AI) for gameplaying, they are often somewhat specialised and computationally inefficient. In this paper, we describe the “ludemic” general game system Ludii, which has the potential to provide an efficient tool for AI researchers as well as game designers, historians, educators and practitioners in related fields. Ludii defines games as structures of ludemes – high-level, easily understandable game concepts – which allows for concise and human-understandable game descriptions. We formally describe Ludii and outline its main benefits: generality, extensibility, understandability and efficiency. Experimentally, Ludii outperforms one of the most efficient Game Description Language (GDL) reasoners, based on a propositional network, in all games available in the Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of performance with the more recently proposed Regular Boardgames (RBG) system, and has various advantages in qualitative aspects such as generality.
Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines - computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. - to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.
In our busy emergency departments the identification of patients at risk of rapid hemodynamic decompensation is crucial. The shock index (SI) (pulse/systolic blood pressure) is a non-invasive clinical sign associated with more complications if higher than 0.7. The effect of β-blockers (BB) and calcium channel blockers (CCB) on the SI has not yet been described. Most studies excluded these patients, owing to the medications' effect on the cardiac pulse. Considering that BB and CCB are commonly prescribed, we studied their effect on the SI's predictability on 30-day mortality in a urosepsis population.
This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training. Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights. We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate significantly improved playing strength in the majority of them after a small number of self-play training games.
The aim of our study was to compare the accuracy of lung sliding identification for the left and right hemithoraxes, using prerecorded short US sequences, in a group of physicians with mixed clinical and US training.A total of 140 US sequences of a complete respiratory cycle were recorded in the operating room. Each sequence was divided in two, yielding 140 sequences of present lung sliding and 140 sequences of absent lung sliding. Of these 280 sequences, 40 were randomly repeated to assess intraobserver variability, for a total of 320 sequences. Descriptive data, the mean accuracy of each participant, as well as the rate of correct answers for each of the original 280 sequences were tabulated and compared for different subgroups of clinical and US training. A video with examples of present and absent lung sliding and a lung pulse was shown before testing.Two sessions were planned to facilitate the participation of 75 clinicians. In the first group, the rate of accurate lung sliding identification was lower in the left hemithorax than in the right (67.0% [interquartile range (IQR), 43.0-83.0] versus 80.0% [IQR, 57.0-95.0]; P < .001). In the second group, the rate of accurate lung sliding identification was also lower in the left hemithorax than in the right (76.3% [IQR, 42.9-90.9] versus 88.7% [IQR, 63.1-96.9]; P = .001). Mean accuracy rates were 67.5% (95% confidence interval, 65.7-69.4) in the first group and 73.1% (95% confidence interval, 70.7-75.5) in the second (P < .001).Lung sliding identification seems less accurate in the left hemithorax when using a short US examination. This study was done on recorded US sequences and should be repeated in a live clinical situation to confirm our results.
