18 research outputs found
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 US317.6, 95%CI: 232.7-402.6) followed by medical costs (US79.2, 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