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.
7. EA SEED - Deep Learning
IMITATION LEARNING WITH CONCURRENT ACTIONS IN 3D GAMES
https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions
8. EA SEED - Deep Learning
IMITATION LEARNING WITH CONCURRENT ACTIONS IN 3D GAMES
https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions
9. EA SEED - Deep Learning
IMITATION LEARNING WITH CONCURRENT ACTIONS IN 3D GAMES
https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions
10. EA SEED - Deep Learning
IMITATION LEARNING WITH CONCURRENT ACTIONS IN 3D GAMES
https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions
11. EA SEED - Deep Learning
IMITATION LEARNING WITH CONCURRENT ACTIONS IN 3D GAMES
https://www.ea.com/seed/news/seed-imitation-learning-concurrent-actions
17. Two Agent Cooperation by DeepMind
Deep Mind: Capture the Flag: the emergence of complex cooperative agents
https://deepmind.com/blog/article/capture-the-flag-science
18. アクター - クリティック・システム
エージェント
Critic ActorTD誤差
環境
報酬r
行動 a
状態 S
行動の結果の報酬を予測するように学習する
木下直人「Actor-Critic によるロボットの強化学習」
http://www.st.nanzan-u.ac.jp/info/gr-thesis/ms/2008/05mm029.pdf
19. Yuxin Wu, Yuandong Tian
"TRAINING AGENT FOR FIRST-PERSON SHOOTER GAME WITH ACTOR-CRITIC CURRICULUM LEARNING“
https://openreview.net/pdf?id=Hk3mPK5gg