Accurately estimating human skill levels is crucial for designing effective human-AI interactions so that AI can provide appropriate challenges or guidance. In games where AI players have beaten top human professionals, strength estimation plays a key role in adapting AI behavior to match human skill levels. In a previous state-of-the-art study, researchers have proposed a strength estimator trained using human players' match data. Given some matches, the strength estimator computes strength scores and uses them to estimate player ranks (skill levels). In this paper, we focus on the observation that human players' behavior tendency varies according to their strength and aim to improve the accuracy of strength estimation by taking this into account. Specifically, in addition to strength scores, we obtain policies for different skill levels from neural networks trained using human players' match data. We then combine features based on these policies with the strength scores to estimate strength. We conducted experiments on Go and chess. For Go, our method achieved an accuracy of 80% in strength estimation when given 10 matches, which increased to 92% when given 20 matches. In comparison, the previous state-of-the-art method had an accuracy of 71% with 10 matches and 84% with 20 matches, demonstrating improvements of 8-9%. We observed similar improvements in chess. These results contribute to developing a more accurate strength estimation method and to improving human-AI interaction.

Why do some people live longer, healthier, and more active lives while others their same age struggle with lifelong chronic pain and suffer maladies up to their dying day--which comes much earlier than others? This basic longevity question has been nagging physicians for ages. The importance of lifestyle factors such as diet, exercise, stress, and epigenetic processes like lifestyle and exposure to environmental hazards have been called into account to explain this divergence, and now a team of researchers from Stanford's Cardiovascular Institute Division of Vascular Surgery and the Buck Institute for Research on Aging believes they found the answer. Rather than the biological age, they say a better predictor of health and longevity is a person's inflammation age. Aided by artificial intelligence and machine learning, the researchers have concluded that epigenetic effects of inflammation processes, particularly on the cardiovascular and neurological level, are connected with much of the morbidity and mortality associated with aging.