The paper’s coauthors note that Mahjong is an imperfect information game with complicated scoring rules. “We believe our techniques designed in Suphx for Mahjong, including global reward prediction, oracle guiding, and … policy adaptation have great potential to benefit for a wide range of real-world applications.” Tackling Mahjong

“Most real-world problems such as finance market predication and logistic optimization share the same characteristics with Mahjong - i.e., complex operation/reward rules, imperfect information,” wrote the paper’s coauthors. Moreover, its researchers assert that the learnings are applicable to other domains, like the enterprise, where mundane but cognitively demanding tasks impact workers’ productivity. Games are an analog of the real world, with a measurable objective, and they can be played an infinite amount of times across hundreds (or thousands) of powerful machines. A revised preprint paper out this week delves a bit deeper, revealing that Suphx - whose performance improved with additional training - is now rated above 99.99% of all ranked human players on Tenhou, a Japan-based global online Mahjong competition platform with over 350,000 members.īuilding superhuman programs for games is a longstanding goal of the AI research community - and not without good reason. Last August, Microsoft Research Asia detailed an AI system dubbed Super Phoenix ( Suphx for short) that could defeat Mahjong players after learning from only 5,000 matches. We're thrilled to announce the return of GamesBeat Summit Next, hosted in San Francisco this October, where we will explore the theme of 'Playing the Edge.' Apply to speak here and learn more about sponsorship opportunities here.

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