The work described in this thesis had two objectives, specifically focusing on people aged 70 years and older: first, we aimed to investigate the associations between several thrombosis-related... Show moreThe work described in this thesis had two objectives, specifically focusing on people aged 70 years and older: first, we aimed to investigate the associations between several thrombosis-related risk factors described in young and middle-aged populations and the risk of venous thrombosis (VT) in the elderly; second, we aimed to provide insight into several long-term consequences (i.e., health-related quality of life (HRQoL) and long-term risk of mortality) after a first VT at old age. Show less
In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These... Show moreIn deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These results have inspired research into artificial general intelligence (AGI).We study table based classic Q-learning on the General Game Playing (GGP) system, showing that classic Q-learning works on GGP, although convergence is slow, and it is computationally expensive to learn complex games.This dissertation uses an AlphaZero-like self-play framework to explore AGI on small games. By tuning different hyper-parameters, the role, effects and contributions of searching and learning are studied. A further experiment shows that search techniques can contribute as experts to generate better training examples to speed up the start phase of training.In order to extend the AlphaZero-likeself-play approach to single player complex games, the Morpion Solitaire game is implemented by combining Ranked Reward method. Our first AlphaZero-based approach is able to achieve a near human best record.Overall, in this thesis, both searching and learning techniques are studied (by themselves and in combination) in GGP and AlphaZero-like self-play systems. We do so for the purpose of making steps towards artificial general intelligence, towards systems that exhibit intelligent behavior in more than one domain. Show less
Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is... Show moreContinuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is essentially about the efficiency: how to obtain the solution with same quality using as minimal resources (e.g., CPU time or memory usage) as possible? In this thesis, the number of function evaluations is considered as the most important resource to save. To achieve this goal, various efforts have been implemented and applied successfully. One research stream focuses on the so-called stochastic variation (mutation) operator, which conducts an (local) exploration of the search space. The efficiency of those operator has been investigated closely, which shows a good stochastic variation should be able to generate a good coverage of the local neighbourhood around the current search solution. This thesis contributes on this issue by formulating a novel stochastic variation that yields good space coverage. Show less
