“But after playing against DeepNash myself, I wasn’t surprised by the top-3 ranking it later achieved on the Gravon platform. I had never heard of an artificial Stratego player that came close to the level needed to win a match against an experienced human player,” study co-author Vincent de Boer said in a statement. “The level of play of DeepNash surprised me. The AI bot even learned to bluff its opponent, using weaker pieces as if they were high-ranking ones, the study noted. The bot won against all the bots with which it played and achieved a “highly competitive level of play” against human expert Stratego players on Gravon – an internet gaming platform and the largest online platform for Stratego.ĭeepNash developed an “unpredictable strategy” to win, scientists say, adding that its initial deployments varied significantly to prevent its opponent recognising the AI’s patterns over a series of games. They tested DeepNash against various state-of-the-art Stratego bots as well as human expert players. In a new study, published in the journal Science last week, scientists reported a bot called DeepNash that has achieved human expert-level performance in the most complex variant of the game, Stratego Classic. The game tests a player’s ability to make relatively slow, deliberative, and logical decisions sequentially.Īll these factors, researchers said, made it difficult for the AI community to master Stratego. They would need to “balance all possible outcomes when making a decision,” scientists explained, adding that the number of potential states – ten to the power of 535 – is also “off the chart” compared with chess, Go, and poker. Players apply strategy and reasoning over a series of sequential actions with no obvious insight into how each of these would contribute to the final outcome. It also means that a successful technique called “game tree search” is not sufficiently scalable. This makes it hard for other AI-based systems to go beyond amateur level.
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