#344 – Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation
Dec 6, 2022
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Noam Brown, a research scientist at Meta AI, co-created systems that outsmart humans in poker and diplomacy. He discusses how AI achieved superhuman levels in these strategic games, highlighting the psychological dynamics of high-stakes poker. Brown explores the interplay between AI and human decision-making, emphasizing emotional engagement and trust in negotiations. He also delves into the future of AI in gaming, particularly its impact on non-playable characters and the ethical implications of replicating human-like decision processes.
AI bots that combine neural networks and search algorithms can achieve superhuman level performance in games like No-Limit Texas Hold'em and Diplomacy.
Search algorithms in game AI enable exploration of different moves, prediction of potential outcomes, and determination of optimal strategies.
AI systems lack generalized planning and reasoning abilities seen in human intuition and search, limiting their flexibility across multiple game types.
AI's success in games like poker has led human players to adapt their strategies and learn from AI tactics.
Developing AI for diplomacy requires understanding human playstyles and incorporating human data to interact effectively.
Trust plays a critical role in diplomacy and establishing trust is a challenge in human-robot interactions.
Deep dives
The Power of AI in Poker
In a landmark competition, the AI bot LaBrodice defeated four of the world's best poker players in 120,000 hands of heads-up no-limit Texas Hold'em. LaBrodice relied on a combination of neural networks and search algorithms to approximate the Nash equilibrium, maximizing its winnings and putting opponents in difficult spots. The AI's ability to think several moves ahead and make optimal decisions based on past strategies and current game states proved to be the winning factor. Search algorithms played a crucial role in poker AI, providing the necessary planning and reasoning abilities to outperform human players.
The Role of Search in Game AI
Search algorithms play a vital role in game AI, as seen in landmark achievements like TD Gammon, Deep Blue, and AlphaGo. Search enables AI systems to explore different moves, predict potential outcomes, and determine optimal strategies. In poker, search algorithms, such as Monte Carlo Tree Search, combined with neural networks, allow bots to evaluate the best actions given a specific hand and game state. By searching potentially complex decision trees, AI can find strategies that maximize expected winnings and put opponents in challenging positions.
The Future of AI and Game AI
While AI has made significant advancements in games like chess, go, and poker, there are still areas for improvement. Generalized planning and reasoning abilities, similar to human intuition and search, are crucial missing pieces. Current AI systems excel in specific games, but lack the flexibility to apply their reasoning across multiple game types. Further advancements in AI could involve developing algorithms capable of general planning, allowing AI to strategize effectively in a variety of game scenarios.
Surprising Results and Human Reactions
The success of AI systems like LaBrodice has surprised many and often leads to a valuable introspection among human players. The ability of AI to exploit weaknesses in opponents and strategically optimize gameplay has led to shifts in human strategies as they attempt to adapt. Many professional players, while initially skeptical, have become willing to learn from AI and incorporate its tactics into their games. The results of AI dominating in games like poker have sparked discussions about the role of human intuition versus strategic computation in achieving success.
The Challenge of Diplomacy AI
Developing an AI for diplomacy poses unique challenges as it involves natural language communication, complex negotiation and cooperation dynamics. Unlike previous game AI breakthroughs, diplomacy requires understanding human playstyles and adapting to them. Incorporating human data was crucial for training the AI to interact effectively with human players.
Training Cicero: Leveraging Human Data
To train the diplomacy AI, a language model was first developed and then made controllable by specifying intents for desired actions. A strategic reasoning model combining reinforcement learning and planning was used to determine optimal actions. Filters were employed to ensure sensible, non-lying messages. The AI was evaluated by comparing its performance to that of human players, using games with players of varied skill levels.
Balancing Trust and Deception
Trust plays a critical role in diplomacy. The AI had to navigate the tension between building trust and the game's inherent lack of trust. Efforts were made to minimize deception and lying, as trust was found to be vital for the AI's long-term success. The significance of trust in diplomacy extends beyond the game and relates to the broader challenge of establishing trust in human-robot interactions.
Beyond Strategy: The Study of Trust
Diplomacy provides a unique domain to investigate the formation of trust, an essential aspect of human-robot interaction. The open-sourcing of models, code, and data by Cicero offers researchers an opportunity to explore the dynamics of trust and deception, contributing to the understanding of how trust can be fostered between intelligent entities.
Importance of diplomacy as a research dataset
Diplomacy, a popular online game, provides a rich dataset for studying human AI interaction, negotiation, trust, and persuasion. With over 50,000 games and 10 million messages, this massive dataset offers valuable opportunities for academic and research communities to explore various research questions.
Impressive performance and key aspects of the bot
The developed bot for diplomacy demonstrated strong performance by ranking second out of 80 players in 40 games played. It showcased proficiency in building connections, persuading other players, coordinating strategies, and tactically understanding opponents' moves. This combination of social and strategic skills showed promising potential for language models interacting with humans in similar settings.
Challenges and ethical concerns in diplomacy and AI
The study highlighted several challenges and ethical considerations. The game's deceptive nature raised questions about developing AI capable of lying to humans. Overcoming the inherent anti-AI bias observed in human players further added to the complexity. The importance of finding the right balance between human-like behavior and ethical guidelines regarding deception and cooperation in AI systems became evident. These findings also underline the broader challenges of data efficiency and the potential for transferability of AI techniques to real-world human negotiation scenarios.
Noam Brown is a research scientist at FAIR, Meta AI, co-creator of AI that achieved superhuman level performance in games of No-Limit Texas Hold’em and Diplomacy. Please support this podcast by checking out our sponsors:
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OUTLINE:
Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time.
(00:00) – Introduction
(05:37) – No Limit Texas Hold ’em
(09:30) – Solving poker
(22:40) – Poker vs Chess
(29:18) – AI playing poker
(1:02:46) – Heads-up vs Multi-way poker
(1:13:37) – Greatest poker player of all time
(1:17:10) – Diplomacy game
(1:27:01) – AI negotiating with humans
(2:09:26) – AI in geopolitics
(2:14:11) – Human-like AI for games
(2:20:12) – Ethics of AI
(2:24:26) – AGI
(2:28:25) – Advice to beginners
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