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[https://en.wikipedia.org/wiki/Intelligent_agent prediction agent] and its application to [[Go]].

Abtract from ''FML-based Prediction Agent and Its Application to Game of Go'' <ref>[[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Chia-Hsiu Kao]], [[Sheng-Chi Yang]], [[Yusuke Nojima]], [[Ryosuke Saga]], [[Nan Shuo]], [[Naoyuki Kubota]] ('''2017'''). ''FML-based Prediction Agent and Its Application to Game of Go''. [https://arxiv.org/abs/1704.04719 arXiv:1704.04719]</ref>:

In this paper, we present a robotic prediction agent including a [https://en.wikipedia.org/wiki/Darkforest darkforest] [[Go]] engine, a [https://en.wikipedia.org/wiki/Fuzzy_markup_language fuzzy markup language] (FML) assessment engine, an FML-based decision support engine, and a [[Robots|robot engine]] for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in [https://en.wikipedia.org/wiki/National_University_of_Tainan NUTN] and [https://en.wikipedia.org/wiki/Osaka_Prefecture_University OPU], for example, the number of [[Monte-Carlo Tree Search|MCTS]] simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO <ref>[https://palro.jp/en/ PALRO is a robot who cares]</ref>, produced by FujiSoft Incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.