Oz Minerals is committed to exploring for Iron Oxide Copper Gold deposits in South Australia's Gawler Craton, which contains world-class systems like Prominent Hill, Carrapateena, and Olympic Dam. In 2012, Oz Minerals planned to spend $90 million on exploration, including drilling at Prominent Hill prospects to extend the open pit mine life beyond 2019, and $29 million exploring the recently acquired Carrapateena project, where 2012 drilling supported the deposit model and indicated extensions of higher grade zones. Geophysics and drilling remain crucial for exploration success in the largely covered Gawler Craton terrain.
Olympic Dam is a large mine in South Australia that extracts copper, uranium, gold, and silver. It is one of the largest copper and uranium mines in the world. The mine employs over 4,000 people and includes both an open pit and underground shaft mine reaching 850 meters deep. BHP Billiton aims to greatly expand operations to extract over 60 million tonnes of ore annually, which would significantly increase production, transportation, and employment at the mine.
Oz Minerals is committed to exploring for Iron Oxide Copper Gold deposits in South Australia's Gawler Craton, which contains world-class systems like Prominent Hill, Carrapateena, and Olympic Dam. In 2012, Oz Minerals planned to spend $90 million on exploration, including drilling at Prominent Hill prospects to extend the open pit mine life beyond 2019, and $29 million exploring the recently acquired Carrapateena project, where 2012 drilling supported the deposit model and indicated extensions of higher grade zones. Geophysics and drilling remain crucial for exploration success in the largely covered Gawler Craton terrain.
Olympic Dam is a large mine in South Australia that extracts copper, uranium, gold, and silver. It is one of the largest copper and uranium mines in the world. The mine employs over 4,000 people and includes both an open pit and underground shaft mine reaching 850 meters deep. BHP Billiton aims to greatly expand operations to extract over 60 million tonnes of ore annually, which would significantly increase production, transportation, and employment at the mine.
The slides of Artificial Intelligence and Entertainment Science (AIES) Workshop 2021 Keynote lecture
https://aies.info/program/
Empathic Entertainment in Digital Game
A digital game give a unique experience to a user. AI system in Digital game consists of three kinds of AI such as Meta-AI, Character AI, and Spatial AI. Game experience is formed by them. Meta-AI keeps watching a status of game and controlling characters, objects, terrain, weather and so on dynamically to make many dramatic and empathic situations in a game for users. Character AI is a brain of an autonomous game character to make a decision by itself, but sometimes it acts to achieve a goal issued from Meta-AI. Spatial AI analyses a terrain and abstracts its features to communicate them to Meta-AI and Character-AI. They can make their intelligent decisions by using specific terrain and environment features. The AI system is called MCS-AI dynamic cooperative model (Meta-AI, Character AI, and Spatial AI dynamic cooperative model). In the lecture, I will explain the system by showing some cases of published digital games.
The slides of Artificial Intelligence and Entertainment Science (AIES) Workshop 2021 Keynote lecture
https://aies.info/program/
Empathic Entertainment in Digital Game
A digital game give a unique experience to a user. AI system in Digital game consists of three kinds of AI such as Meta-AI, Character AI, and Spatial AI. Game experience is formed by them. Meta-AI keeps watching a status of game and controlling characters, objects, terrain, weather and so on dynamically to make many dramatic and empathic situations in a game for users. Character AI is a brain of an autonomous game character to make a decision by itself, but sometimes it acts to achieve a goal issued from Meta-AI. Spatial AI analyses a terrain and abstracts its features to communicate them to Meta-AI and Character-AI. They can make their intelligent decisions by using specific terrain and environment features. The AI system is called MCS-AI dynamic cooperative model (Meta-AI, Character AI, and Spatial AI dynamic cooperative model). In the lecture, I will explain the system by showing some cases of published digital games.
The document discusses the differences between making a microwave and creating artificial intelligence. It explores how intelligence may have common principles across different animals and how studying biology can help understand intelligence and realize it in computers and robots. It also discusses approaches to building AI through engineering as well as understanding what intelligence is through philosophy and science. Finally, it discusses game engines and their role in simulating physical, chemical, economic, social and biological rules to create virtual worlds.
59. 強化学習
(例)格闘ゲームTaoFeng におけるキャラクター学習
Ralf Herbrich, Thore Graepel, Joaquin Quiñonero Candela Applied Games Group,Microsoft Research Cambridge
"Forza, Halo, Xbox Live The Magic of Research in Microsoft Products"
http://research.microsoft.com/en-us/projects/drivatar/ukstudentday.pptx
Microsoft Research Playing Machines: Machine Learning Applications in Computer Games
http://research.microsoft.com/en-us/projects/mlgames2008/
Video Games and Artificial Intelligence
http://research.microsoft.com/en-us/projects/ijcaiigames/
62. CORE Layer は、Physical Laryer 、Mission Layer のうちで、
どの認識を生成するかを決定するコマンドを投げる。
CERA-CRANIUM認識モデル
Arrabales, R. Ledezma, A. and Sanchis, A. "Towards the Generation of Visual Qualia
in Artificial Cognitive Architectures". (2010)
http://www.conscious-robots.com/raul/papers/Arrabales_BICS2010.pdf
134. Deep Q-Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves,
Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (DeepMind Technologies)
Playing Atari with Deep Reinforcement Learning
http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
画面を入力
操作はあらかじめ教える
スコアによる強化学習
138. 学習過程解析
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves,
Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (DeepMind Technologies)
Playing Atari with Deep Reinforcement Learning
http://www.cs.toronto.edu/~vmnih/docs/dqn.pdf