Machine learning systems have been annihilating human opponents for well over a decade now (the first Watson Jeopardy win was in 2011), though the types of games they excel at are rather limited.
Typically competitive board or video games with a limited playfield, sequential moves, and at least one clearly defined opponent, any game requiring number crunching is advantageous.
Diplomacy, on the other hand, requires very little computation, instead requiring players to negotiate directly with their opponents and make respective plays at the same time — something that modern ML systems are generally not designed to do.
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However, Meta researchers have created an AI agent capable of negotiating global policy positions as well as any UN ambassador.
Diplomacy was released in 1959 and functions similarly to a more refined version of RISK, with two to seven players assuming the roles of European powers and attempting to win the game by conquering their opponents’ territories.
Unlike RISK, where the outcome of conflicts is determined by a simple roll of the dice, Diplomacy requires players to first negotiate with one another — forming alliances, backstabbing, and all that good stuff — before everyone moves their pieces at the same time during the next game phase.
Reading and manipulating opponents, convincing players to form alliances and plan complex strategies, navigating delicate partnerships, and knowing when to switch sides are all important aspects of the game — and all skills that machine-learning systems generally lack.
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On Wednesday, Meta AI researchers announced that they had overcome machine learning shortcomings with CICERO, the first AI to demonstrate human-level Diplomacy performance.
The team trained Cicero on 2.7 billion parameters over 50,000 rounds at webDiplomacy.net, an online version of the game, where it finished second (out of 19 participants) in a 5-game league tournament while doubling its opponents’ average score.
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