Two new papers from AI powerhouses DeepMind and Meta describe how AI systems are notching wins against human players in complex games involving deception, negotiation and cooperation. Why it matters: Machine contenders have struggled with games where information is incomplete or hidden from players -- similar to the intentions of humans in daily life and interactions. Driving the news: Researchers from DeepMind outline a new autonomous agent called "DeepNash" that learned to play the game Stratego in a paper published today in Science. How they did it: The DeepMind team combined an algorithm for learning the game through self-play and another that steers that learning toward an optimal strategy. Meta researchers last week described an AI system called "Cicero" that they report can play the game Diplomacy at the level of humans.

While artificial intelligence long ago surpassed human capability in Chess, and more recently Go -- and let us not forget Doom -- other more complex board games still present a challenge to computer systems. Until very recently, Stratego and Diplomacy were two of those games, but now AI has become table-flipping good at the former and passably human at the latter. On the surface, you might think that it's just because these games require a certain level of long-term planning and strategy. But so do Go and Chess, just in a different way. The crucial difference is actually that Stratego and Diplomacy are games of strategy based on imperfect information.