Machine learning systems have been mopping the floor with their human opponents for well over a decade now (seriously, that first Watson Jeopardy win was all the way back in 2011), though the types of games they excel at are rather limited. Typically competitive board or video games using a limited play field, sequential moves and at least one clearly-defined opponent, any game that requires the crunching of numbers is to their advantage. Diplomacy, however, requires very little computation, instead demanding players negotiate directly with their opponents and make respective plays simultaneously — things modern ML systems are generally not built to do. But that hasn't stopped Meta researchers from designing an AI agent that can negotiate global policy positions as well as any UN ambassador.
Diplomacy was first released in 1959 and works like a more refined version of RISK where between two and seven players assume the roles of a European power and attempt to win the game by conquering their opponents' territories. Unlike RISK where the outcome of conflicts are decided by a simple the roll of the dice, Diplomacy demands players first negotiate with one another — setting up alliances, backstabbing, all that good stuff — before everybody moves their pieces simultaneously during the following game phase. The abilities to read and manipulate opponents, convince players to form alliances and plan complex strategies, navigate delicate partnerships and know when to switch sides, are all a huge part of the game — and all skills that machine learning systems generally lack.
On Wednesday, Meta AI researchers announced that they had surmounted those machine learning shortcomings with CICERO, the first AI to display human-level
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