TAG: A Tabletop Games Framework

Esta ponencia forma parte de : 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)Tabletop games come in a variety of forms, including board games, card games, and dice games. In recent years, their complexity has considerably increased, with many components, rules that change dynamically through the game, diverse player roles, and a series of control parameters that influence a game’s balance. As such, they also encompass novel and intricate challenges for Artificial Intelligence methods, yet research largely focuses on classical board games such as chess and Go. We introduce in this work the Tabletop Games (TAG) framework, which promotes research into general AI in modern tabletop games, facilitating the implementation of new games and AI players, while providing analytics to capture the complexities of the challenges proposed. We include preliminary results with sample AI players, showing some moderate success, with plenty of room for improvement, and discuss further developments and new research direction

AutoRL Hyperparameter Landscapes

Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes that hyperparameter optimization (HPO) methods traverse in search of optimal configurations. In view of existing AutoRL approaches dynamically adjusting hyperparameter configurations, we propose an approach to build and analyze these hyperparameter landscapes not just for one point in time but at multiple points in time throughout training. Addressing an important open question on the legitimacy of such dynamic AutoRL approaches, we provide thorough empirical evidence that the hyperparameter landscapes strongly vary over time across representative algorithms from RL literature (DQN and SAC) in different kinds of environments (Cartpole and Hopper). This supports the theory that hyperparameters should be dynamically adjusted during training and shows the potential for more insights on AutoRL problems that can be gained through landscape analyses

The economic burden of households affected by tuberculosis in Brazil: First national survey results, 2019-2021.

BackgroundOne of the three main targets of the World Health Organization (WHO) End TB Strategy (2015-2035) is that no tuberculosis (TB) patients or their households face catastrophic costs (defined as exceeding 20% of the annual household income) because of the disease. Our study seeks to determine, as a baseline, the magnitude and main drivers of the costs associated with TB disease for patients and their households and to monitor the proportion of households experiencing catastrophic costs in Brazil.MethodsA national cross-sectional cluster-based survey was conducted in Brazil in 2019-2021 following WHO methodology. TB patients of all ages and types of TB were eligible for the survey. Adult TB patients and guardians of minors (ResultsWe interviewed 603 patients, including 538 (89%) with drug-sensitive (DS) and 65 (11%) with drug-resistant (DR) TB. Of 603 affected households, 48.1% (95%CI: 43-53.2) experienced costs above 20% of their annual household income during their TB episode. The proportion was 44.4% and 78.5% among patients with DS- and DR-TB, respectively. On average, patients incurred costs of US$1573 (95$317.6, 95%CI: 232.7-402.6) followed by medical costs (US$85.5, 95$79.2, 95%CI: 61.9-96.5). In multivariate analysis, predictors of catastrophic costs included positive HIV status (aOR = 3.0, 95%CI:1.1-8.6) and self-employment (aOR = 2.7, 95%CI:1.1-6.5); high education was a protective factor (aOR = 0.1, 95%CI:0.0-0.9).ConclusionsAlthough the services offered to patients with TB are free of charge in the Brazilian public health sector, the availability of free diagnosis and treatment services does not alleviate patients' financial burden related to accessing TB care. The study allowed us to identify the costs incurred by patients and suggest actions to mitigate their suffering. In addition, this study established a baseline for monitoring catastrophic costs and fostering a national policy to reduce the costs to patients for TB care in Brazil