Septic Arthritis Caused by Legionella dumoffii in a Patient with Systemic Lupus Erythematosus-Like Disease

We describe a patient with systemic lupus erythematosus (SLE)-like disease on immunosuppressive treatment who developed septic arthritis of the knee involving Legionella dumoffii. Cultures initially remained negative. A broad-range 16S PCR using synovial fluid revealed L. dumoffii rRNA genes, a finding that was subsequently confirmed by positive Legionella culture results

Automatic Goal Discovery in Subgoal Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that can play a wide range of games without requiring any domain-specific knowledge. However, MCTS tends to struggle in very complicated games due to an exponentially increasing branching factor. A promising solution for this problem is to focus the search only on a small fraction of states. Subgoal Monte Carlo Tree Search (S-MCTS) achieves this by using a predefined subgoal-predicate that detects promising states called subgoals. However, not only does this make S-MCTS domain-dependent, but also it is often difficult to define a good predicate. In this paper, we propose using quality diversity (QD) algorithms to detect subgoals in real-time. Furthermore, we show how integrating QD-algorithms into S-MCTS significantly improves its performance in the Physical Travelling Salesmen Problem without requiring any domain-specific knowledge

Game State and Action Abstracting Monte Carlo Tree Search for General Strategy Game-Playing

When implementing intelligent agents for strategy games, we observe that search-based methods struggle with the complexity of such games. To tackle this problem, we propose a new variant of Monte Carlo Tree Search which can incorporate action and game state abstractions. Focusing on the latter, we developed a game state encoding for turn-based strategy games that allows for a flexible abstraction. Using an optimization procedure, we optimize the agent's action and game state abstraction to maximize its performance against a rule-based agent. Furthermore, we compare different combinations of abstractions and their impact on the agent's performance based on the Kill the King game of the Stratega framework. Our results show that action abstractions have improved the performance of our agent considerably. Contrary, game state abstractions have not shown much impact. While these results may be limited to the tested game, they are in line with previous research on abstractions of simple Markov Decision Processes. The higher complexity of strategy games may require more intricate methods, such as hierarchical or time-based abstractions, to further improve the agent's performance

PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games

In recent years, Game AI research has made important breakthroughs using Reinforcement Learning (RL). Despite this, RL for modern tabletop games has gained little to no attention, even when they offer a range of unique challenges compared to video games. To bridge this gap, we introduce PyTAG, a Python API for interacting with the Tabletop Games framework (TAG). TAG contains a growing set of more than 20 modern tabletop games, with a common API for AI agents. We present techniques for training RL agents in these games and introduce baseline results after training Proximal Policy Optimisation algorithms on a subset of games. Finally, we discuss the unique challenges complex modern tabletop games provide, now open to RL research through PyTAG

Generating Diverse and Competitive Play-Styles for Strategy Games

Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training

Infrared imaging and acoustic sizing of a bubble inside a MEMS piezo ink channel

Piezo drop-on-demand inkjet printers are used in an increasing number of applications because of their reliable deposition of droplets onto a substrate. Droplets of a few picoliters are ejected from an inkjet nozzle at frequencies of up to 100 kHz. However, the entrapment of an air microbubble in the ink channel can severely impede the productivity and reliability of the printing system. The air bubble disturbs the channel acoustics, resulting in disrupted drop formation or failure of the jetting process. Here we study a micro-electro-mechanical systems-based printhead. By using the actuating piezo transducer in receive mode, the acoustical field inside the channel was monitored, clearly identifying the presence of an air microbubble inside the channel during failure of the jetting process. The infrared visualization technique allowed for the accurate sizing of the bubble, including its dynamics, inside the intact printhead. A model was developed to calculate the mutual interaction between the channel acoustics and the bubble dynamics. The model was validated by simultaneous acoustical and infrared detection of the bubble. The model can predict the presence and size of entrapped air bubbles inside an operating ink channel purely from the acoustic response

Sticky bubbles

We discuss the physical forces that are required to remove an air bubble immersed in a liquid from a corner. This is relevant for inkjet printing technology, as the presence of air bubbles in the channels of a printhead perturbs the jetting of droplets. A simple strategy to remove the bubble is to ush the ink past the bubble by providing a high pressure pulse. In this report we rst compute the viscous drag forces that such a ow exerts on the bubble. Then, we compare this to the \sticking forces" on the bubble, due to the capillary interaction with the wall. From this we can estimate the required ow velocities for bubble removal, as a function of channel geometry, contact angle and ink properties. Finally, we investigate other ways to exert a force on a trapped bubble. In particular we focus on forces induced by electric elds which can alter the contact angle of the drop, or by locally applying thermal gradients. Once again, these forces are compared to the sticking forces to identify the parameters where the bubble can be removed