Exercise and lifestyle predictors of resting heart rate in healthy young adults

Physical exercise is well-understood to provide significant health benefits, through physiological adaptations induced by the repeated exertion stress exercise imposes on our systems. Chief among these are cardiovascular adaptations to exercise, including adjustments of cardiac parameters such as stroke volume, heart rate, and maximal cardiac output. It is commonly assumed that aerobic forms of exercise provide greater cardiovascular benefits than do non-aerobic forms of exercise. To test this assumption, exercise habits and resting heart rate were examined in a large population of healthy young adults. 90% of subjects reported regular physical exercise, with aerobic exercise constituting 64% of all exercise hours. Subjects with a history of smoking exhibited higher resting heart rates than those with no smoking history, an effect which was due primarily to a reduction in exercise hours by smokers than due to a smoking habit itself. While both total exercise amount and aerobic exercise amount were significantly and negatively related to resting heart rate, total exercise amount was a better overall predictor of resting heart rate than was aerobic exercise amount. All forms of exercise were associated with cardiovascular health, with cardiovascular benefits accruing according to the amount of exercise performed, even in optimally healthy young adults

Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi

Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche. We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner's behavioral niche can be correctly inferred, but in practice a partner's behavior depends and interferes with the meta-agent's own behavior, suggesting an avenue for future research in characterizing another agent's behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384

Shape manipulation using physically based wire deformations

This paper develops an efficient, physically based shape manipulation technique. It defines a 3D model with profile curves, and uses spine curves generated from the profile curves to control the motion and global shape of 3D models. Profile and spine curves are changed into profile and spine wires by specifying proper material and geometric properties together with external forces. The underlying physics is introduced to deform profile and spine wires through the closed form solution to ordinary differential equations for axial and bending deformations. With the proposed approach, global shape changes are achieved through manipulating spine wires, and local surface details are created by deforming profile wires. A number of examples are presented to demonstrate the applications of our proposed approach in shape manipulation

Pitako -- Recommending Game Design Elements in Cicero

Recommender Systems are widely and successfully applied in e-commerce. Could they be used for design? In this paper, we introduce Pitako1, a tool that applies the Recommender System concept to assist humans in creative tasks. More specifically, Pitako provides suggestions by taking games designed by humans as inputs, and recommends mechanics and dynamics as outputs. Pitako is implemented as a new system within the mixed-initiative AI-based Game Design Assistant, Cicero. This paper discusses the motivation behind the implementation of Pitako as well as its technical details and presents usage examples. We believe that Pitako can influence the use of recommender systems to help humans in their daily tasks.Comment: Paper accepted in the IEEE Conference on Games 2019 (COG 2019

Exercise and lifestyle predictors of resting heart rate in healthy young adults

Physical exercise is well-understood to provide significant health benefits, through physiological adaptations induced by the repeated exertion stress exercise imposes on our systems.  Chief among these are cardiovascular adaptations to exercise, including adjustments of cardiac parameters such as stroke volume, heart rate, and maximal cardiac output.  It is commonly assumed that aerobic forms of exercise provide greater cardiovascular benefits than do non-aerobic forms of exercise.  To test this assumption, exercise habits and resting heart rate were examined in a large population of healthy young adults.  90% of subjects reported regular physical exercise, with aerobic exercise constituting 64% of all exercise hours.  Subjects with a history of smoking exhibited higher resting heart rates than those with no smoking history, an effect which was due primarily to a reduction in exercise hours by smokers than due to a smoking habit itself.  While both total exercise amount and aerobic exercise amount were significantly and negatively related to resting heart rate, total exercise amount was a better overall predictor of resting heart rate than was aerobic exercise amount.  All forms of exercise were associated with cardiovascular health, with cardiovascular benefits accruing according to the amount of exercise performed, even in optimally healthy young adults

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018