Lost in Draft: Investigating Game Balance in Multiplayer Online Battle Arena Drafting

Master´s thesis in Information and Communication Technology (IKT590) University of Agder, GrimstadThis thesis explores modern machine learning solutions to turn-basedstrategy games. In particular, we explore the possibilities of equalizing the playing field for both teams in the draft phase of Defense of the Ancients 2 (Dota 2) and League of Legends (LoL), with both games being giants in the multi-million dollar esports industry. The thesis covers the Multiplayer Online Battle Arena video game genre and the draft phase the games use. We also discuss the tech-nology used to address the problem, as well as the basic concepts of modern machine learning that allowed this technology to arise. We then introduce the Win Rate Predictor, which is our implementation of the reward function in the Monte Carlo Tree Search algorithm used to predict the win rate of each team given different parameters in the draft phase. The results show clear and quantifiable differences in differentparts of the draft phase. This includes reordering the pick order, the impact of including banning in the draft phase, and the balance ofdifferent draft schemes. Specifically, first pick has a higher win rate than last pick for the majority of the draft schemes, suggesting that strong initial picks aremore valuable than reactive response picks. Additionally, bans can bea way to influence the balance of a draft phase. Our simulations also suggest that the southwestern locations on the map have a higher win rate in both Dota 2 and LoL. And finally, according to our simulations,the games’ respective implementation of a draft scheme is the most evenly balanced draft scheme for their game

Oral History of Hao Nhien Vu

An oral history with Mr. Hao Nhien Vu, born in 1964 in Saigon, Vietnam. He grew up in Saigon before and during the Vietnam War. Both of his parents were researchers and microbiologists and tried to use their work connections to escape Vietnam after the war, but without success. However, his two brothers were able to escape Saigon in 1975. When the war ended, he was just finishing elementary school and continued onto middle school, but he had to transfer to multiple schools because the new government was shutting down these schools post-1975. He eventually went to Marie Curie High School in Saigon, which was previously an all-girl’s school before 1975. He flew with his parents out of Vietnam with the aid of a sponsor to Bangkok, then to Hong Kong, Japan, Seattle, and finally settling in West Lafayette, Indiana. Mr. Vu attended Purdue University where he graduated with a Masters in Mathematics and eventually obtained a job as an actuary at an insurance company in Indianapolis. He then moved to Los Angeles, California where he worked as an actuary at Allied Insurance. Afterwards, he decided to pursue a law degree at UCLA School of Law. He then became a journalist for Nguoi Viet Daily News and started teaching at various community colleges. He currently teaches math full-time at Coastline Community College and lives with his wife in Fountain Valley, California.Recorded Digitall

The Law of Treaties and the Export of Hazardous Waste

[No abstract

The Law of Treaties and the Export of Hazardous Waste

[No abstract

A Response to Soule and Weinstein: National Organization for Women v. Scheidler Is Just Hard Facts Making Bad Law

[No abstract

Lost in Draft: Investigating Game Balance in Multiplayer Online Battle Arena Drafting

This thesis explores modern machine learning solutions to turn-basedstrategy games. In particular, we explore the possibilities of equalizing the playing field for both teams in the draft phase of Defense of the Ancients 2 (Dota 2) and League of Legends (LoL), with both games being giants in the multi-million dollar esports industry. The thesis covers the Multiplayer Online Battle Arena video game genre and the draft phase the games use. We also discuss the tech-nology used to address the problem, as well as the basic concepts of modern machine learning that allowed this technology to arise. We then introduce the Win Rate Predictor, which is our implementation of the reward function in the Monte Carlo Tree Search algorithm used to predict the win rate of each team given different parameters in the draft phase. The results show clear and quantifiable differences in differentparts of the draft phase. This includes reordering the pick order, the impact of including banning in the draft phase, and the balance ofdifferent draft schemes. Specifically, first pick has a higher win rate than last pick for the majority of the draft schemes, suggesting that strong initial picks aremore valuable than reactive response picks. Additionally, bans can bea way to influence the balance of a draft phase. Our simulations also suggest that the southwestern locations on the map have a higher win rate in both Dota 2 and LoL. And finally, according to our simulations,the games’ respective implementation of a draft scheme is the most evenly balanced draft scheme for their game

Effect of double dose oseltamivir on clinical and virological outcomes in children and adults admitted to hospital with severe influenza: Double blind randomised controlled trial

10.1136/bmj.f3039BMJ (Online)3467911-BMJO