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AI for Sports

為了解決DeepMind paper的問題，作者Brady, Chris,Tuyls, Karl,Omidshafiei, Shayegan 這樣論述：

Professor Chris Brady is currently the Chief Intelligence Officer at Sportsology, a US-based consultancy to elite sports organizations across the globe. Professor Brady has had a varied working life ranging from a line-worker at Chrysler in Detroit in his teens to managing a bookmaker’s shop, from a

land surveyor to a semi-professional footballer, from a naval officer to a management consultant. Prior to joining Sportsology he was most recently Professor of Management Studies at Salford University where he founded the Centre for Sports Business which focussed on the production of high quality

research with a particular emphasis on statistical analytics and future trend analysis within the global sports industry.Karl Tuyls (FBCS) is a team lead at DeepMind (Paris, France), an honorary professor of Computer Science at the University of Liverpool, UK, and a Guest Professor at the University

of Leuven, Belgium. Previously, he held academic positions at the Vrije Universiteit Brussel, Hasselt University, Eindhoven University of Technology, and Maastricht University. Prof. Tuyls has received several awards with his research, amongst which: the Information Technology prize 2000 in Belgium

, best demo award at AAMAS’12, winner of various Robocup@Work competitions (’13, ’14), and he was a co-author of the runner-up best paper award at ICML’18. Furthermore, his research has received substantial attention from national and international press and media, most recently his work on Sports A

nalytics featured in Wired UK. He is a fellow of the British Computer Society (BCS), is on the editorial board of the Journal of Autonomous Agents and Multi-Agent Systems, and is editor-in-chief of the Springer briefs series on Intelligent Systems. Prof. Tuyls is also an emeritus member of the board

of directors of the International Foundation for Autonomous Agents and Multiagent Systems.Shayegan Omidshafiei is a senior research scientist in DeepMind’s Game Theory team, where he also co-leads DeepMind’s Sports Analytics effort. His research interests include multiagent systems, reinforcement l

earning, robotics, and control systems. He previously received his Ph.D. at the Laboratory for Information and Decision Systems (LIDS) and Aerospace Controls Laboratory (ACL) at MIT. He received a B.A.Sc. degree from the University of Toronto in 2012, and an S.M. degree in Aeronautics and Astronauti

cs from MIT in 2015. He is co-inventor of 5 patents filed with the United States Patent Office.

AI for Sports

為了解決DeepMind paper的問題，作者Brady, Chris,Tuyls, Karl,Omidshafiei, Shayegan 這樣論述：

Professor Chris Brady is currently the Chief Intelligence Officer at Sportsology, a US-based consultancy to elite sports organizations across the globe. Professor Brady has had a varied working life ranging from a line-worker at Chrysler in Detroit in his teens to managing a bookmaker’s shop, from a

land surveyor to a semi-professional footballer, from a naval officer to a management consultant. Prior to joining Sportsology he was most recently Professor of Management Studies at Salford University where he founded the Centre for Sports Business which focussed on the production of high quality

research with a particular emphasis on statistical analytics and future trend analysis within the global sports industry.Karl Tuyls (FBCS) is a team lead at DeepMind (Paris, France), an honorary professor of Computer Science at the University of Liverpool, UK, and a Guest Professor at the University

of Leuven, Belgium. Previously, he held academic positions at the Vrije Universiteit Brussel, Hasselt University, Eindhoven University of Technology, and Maastricht University. Prof. Tuyls has received several awards with his research, amongst which: the Information Technology prize 2000 in Belgium

, best demo award at AAMAS’12, winner of various Robocup@Work competitions (’13, ’14), and he was a co-author of the runner-up best paper award at ICML’18. Furthermore, his research has received substantial attention from national and international press and media, most recently his work on Sports A

nalytics featured in Wired UK. He is a fellow of the British Computer Society (BCS), is on the editorial board of the Journal of Autonomous Agents and Multi-Agent Systems, and is editor-in-chief of the Springer briefs series on Intelligent Systems. Prof. Tuyls is also an emeritus member of the board

of directors of the International Foundation for Autonomous Agents and Multiagent Systems.Shayegan Omidshafiei is a senior research scientist in DeepMind’s Game Theory team, where he also co-leads DeepMind’s Sports Analytics effort. His research interests include multiagent systems, reinforcement l

earning, robotics, and control systems. He previously received his Ph.D. at the Laboratory for Information and Decision Systems (LIDS) and Aerospace Controls Laboratory (ACL) at MIT. He received a B.A.Sc. degree from the University of Toronto in 2012, and an S.M. degree in Aeronautics and Astronauti

cs from MIT in 2015. He is co-inventor of 5 patents filed with the United States Patent Office.

應用多智慧體強化學習優化自駕巴士營運之研究

為了解決DeepMind paper的問題，作者王嵩容 這樣論述：

自駕巴士過去五年在全世界超過七十幾個城市推廣測試，先進的無人駕駛技術得以免除駕駛人力成本、降低交通事故，有很大的潛力為公共運輸系統帶來革命性的變革。本研究的目標是發展一個自駕巴士車隊管理模式，讓自駕巴士能夠有效率的運作，同時比較自駕巴士的相對優勢。考量自駕巴士本質上是去中央指派的獨立運作智慧體，這些智慧體所面對的是旅客隨機到達以及部份環境資訊，在沒有人為干涉的狀況下學習作出決策。本研究基於自駕巴士在單一路動態服務特性而發展了一個「多智慧體強化學習」(MARL)車隊派遣方法，結合最先進的策略梯度(PG)演算法，來解決複雜且動態的自駕巴士車隊派遣最佳化問題。本研究同時發展一個自駕巴士往返路線的智

慧體動態模擬平台，用來訓練和評估此強化學習派遣演算法的績效。模擬結果顯示，本研究所發展的自駕巴士強化學習派遣，在相對較低乘客需求狀況，比較現行固定派遣公車具有降低車隊規模和減少乘客等待時間的優勢。本研究也同時探討自駕巴士強化學習派遣在一條往返公車路線上的社會總成本，包括業者營運成本以及乘客等待時間成本與乘客車內時間成本。研究結果顯示自駕巴士人工智慧派遣在社會成本最佳化狀況下，較現行固定班表普通公車具有明顯的成本優勢。研究成果可以作為單一路廊自駕巴士派遣、優化以及未來自駕巴士系統發展的基礎。