DeepMind Cicero research
paperAuthors
Credibility Rating
Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.
Rating inherited from publication venue: Science
DeepMind's Cicero demonstrates an AI system achieving human-level performance in complex multi-agent negotiation through language, raising important questions about AI systems' capabilities in persuasion, deception detection, and multi-agent coordination in competitive-cooperative environments.
Paper Details
Metadata
Abstract
Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players’ beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game. Description AI masters Diplomacy The game Diplomacy has been a major challenge for artificial intelligence (AI). Unlike other competitive games that AI has recently mastered, such as chess, Go, and poker, Diplomacy cannot be solved purely through self-play; it requires the development of an agent to understand other players’ motivations and perspectives and to use natural language to negotiate complex shared plans. The Meta Fundamental AI Research Diplomacy Team (FAIR) et al. developed an agent that is able to play the full natural language form of the game and demonstrates performance well above the human average in an online Diplomacy league. The present work has far-reaching implications for the development of cooperative AI and language models for communication with people, even when interactions involve a mixture of aligned and competing interests. —YS Artificial intelligence demonstrates human-level performance in the strategic board game Diplomacy.
Summary
DeepMind's Cicero is the first AI agent to achieve human-level performance in Diplomacy, a complex strategy game requiring natural language negotiation and cooperation among seven players. The system integrates a language model with planning and reinforcement learning to infer other players' beliefs and intentions from conversations while generating strategic dialogue. In 40 games of an anonymous online league, Cicero scored more than double the average human player and ranked in the top 10% of experienced participants, demonstrating significant advances in AI communication and cooperative reasoning in mixed-motive environments.
Cached Content Preview
[Skip to main content](https://www.science.org/doi/10.1126/science.ade9097#main-content-focus)
Advertisement
Main content starts here
Contents
## AI masters Diplomacy
The game _Diplomacy_ has been a major challenge for artificial intelligence (AI). Unlike other competitive games that AI has recently mastered, such as chess, Go, and poker, _Diplomacy_ cannot be solved purely through self-play; it requires the development of an agent to understand other players’ motivations and perspectives and to use natural language to negotiate complex shared plans. The Meta Fundamental AI Research Diplomacy Team (FAIR) _et al_. developed an agent that is able to play the full natural language form of the game and demonstrates performance well above the human average in an online _Diplomacy_ league. The present work has far-reaching implications for the development of cooperative AI and language models for communication with people, even when interactions involve a mixture of aligned and competing interests. —YS
## Abstract
Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in _Diplomacy_, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players’ beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online _Diplomacy_ league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.
## Register and access this article for free
As a service to the community, this article is available for free.
[Log in](https://www.science.org/action/ssostart?redirectUri=/doi/10.1126/science.ade9097) [Create a free account](https://purchase.aaas.org/order/startnew/580?&returnUrl=https://www.science.org/doi/10.1126/science.ade9097&ctc=SPREGFRE)
## Access the full article
View all access options to continue reading this article.
[CHECK ACCESS](https://www.science.org/doi/10.1126/science.ade9097#core-collateral-purchase-access)
## Supplementary Materials
### This PDF file includes:
Materials and Methods
Figs. S1 to S10
Tables S1 to S15
References ( [_35_](https://www.science.org/doi/10.1126/science.ade9097#R35)– [_90_](https://www.science.org/doi/10.1126/science.ade9097#R90))
- [Download](https://www.science.org/doi/suppl/10.1126/science.ade9097/suppl_file/science.ade9097_sm.pdf)
- 1.99 MB
## References and Notes
1
T. Brown et al., Language models are few-shot learners. _Adv. Neural Inf. Process. Syst._ **33**, 1877 (2020).
[Google Scholar](https://scholar
... (truncated, 76 KB total)ace4ffed73915272 | Stable ID: YjcyNDY4Mz