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This week's newsletter delves into the world of AI in human-computer gaming, spotlighting techniques, challenges, and opportunities. Explore the dynamic landscape where artificial intelligence and gaming intersect for a brief yet insightful overview.

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AI in Human-computer Gaming

• Human-computer gaming has been a crucial arena for testing AI technologies, dating back to the Turing test in 1950.

• Notable milestones include the development of checkers AI Chinook (1989) and IBM's Deep Blue defeating chess grandmaster Garry Kasparov in 1997.

Evolution of AI in Gaming:

• Recent years have witnessed rapid advancements in human-computer gaming AIs, including DQN, AlphaGo, Libratus, OpenAI Five, AlphaStar, and others.

• These AIs employ modern techniques to challenge professional human players, showcasing significant progress in decision-making intelligence.

Key Games and AIs:

• The paper covers four game types and their corresponding AIs: board games (AlphaGo series), card games (DeepStack, Libratus, Suphx, DouZero), FPS games (FTW), and RTS games (AlphaStar, OpenAI Five, JueWu).

• Notable breakthroughs in StarCraft, Dota2, Honor of Kings, Mahjong, and more.

Challenges in Decision-Making Intelligence:

• Identified key factors challenging intelligent decision-making: imperfect information, long time horizon, in-transitive games, and multi-agent cooperation.

Sections of the Paper: [Link]

• Section 3 (Board Game AIs): Discusses AlphaGo, AlphaGo Zero, and AlphaZero, emphasizing Monte Carlo tree search and deep learning techniques.

• Section 4 (Card Game AIs): Highlights DeepStack and Libratus in heads-up no-limit Texas hold'em, with a focus on counterfactual regret minimization. Introduces Suphx and DouZero for Mahjong and DouDiZhu.

• Section 5 (FPS Game AIs): Introduces FTW, an AI for Quake III Arena's capture the flag mode, showcasing human-level performance based on pixels and game points.

• Section 6 (RTS Game AIs): Discusses AlphaStar's grandmaster level in StarCraft, Commander's lightweight version, and OpenAI Five's success in Dota2. JueWu becomes the first AI for full RTS games in Honor of Kings.

Comparison of Techniques:

• Current breakthroughs primarily use two categories of techniques: tree search with self-play and distributed deep reinforcement learning with self-play or population-play.

• Emphasis on reaching Nash equilibrium and achieving general technology.

Challenges and Future Trends:

• Current AIs have limitations: designed for specific games, resource-intensive training, and evaluations based on limited professional players.

• Future research directions include addressing limitations, experimenting with disturbances in games, reducing computational resource requirements, and ensuring AIs' adaptability across different game maps.

Conclusion:

The paper aims to provide a comprehensive overview of techniques, challenges, and opportunities in the field of human-computer gaming AIs, inspiring both beginners and seasoned researchers for deeper exploration.

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Flipped.ai Editorial Team