Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy

János Kramár, Tom Eccles, Ian Gemp, Andrea Tacchetti, Kevin R. McKee, Mateusz Malinowski, Thore Graepel and Yoram Bachrach ()
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János Kramár: DeepMind
Tom Eccles: DeepMind
Ian Gemp: DeepMind
Andrea Tacchetti: DeepMind
Kevin R. McKee: DeepMind
Mateusz Malinowski: DeepMind
Thore Graepel: Altos Labs
Yoram Bachrach: DeepMind

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract The success of human civilization is rooted in our ability to cooperate by communicating and making joint plans. We study how artificial agents may use communication to better cooperate in Diplomacy, a long-standing AI challenge. We propose negotiation algorithms allowing agents to agree on contracts regarding joint plans, and show they outperform agents lacking this ability. For humans, misleading others about our intentions forms a barrier to cooperation. Diplomacy requires reasoning about our opponents’ future plans, enabling us to study broken commitments between agents and the conditions for honest cooperation. We find that artificial agents face a similar problem as humans: communities of communicating agents are susceptible to peers who deviate from agreements. To defend against this, we show that the inclination to sanction peers who break contracts dramatically reduces the advantage of such deviators. Hence, sanctioning helps foster mostly truthful communication, despite conditions that initially favor deviations from agreements.

Date: 2022
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DOI: 10.1038/s41467-022-34473-5

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