In this paper, we present a robotic prediction agent including a [darkforest](https://en.wikipedia.org/wiki/Darkforest) [Go](Go "Go") engine, a [fuzzy markup language](https://en.wikipedia.org/wiki/Fuzzy_markup_language) (FML) assessment engine, an FML-based decision support engine, and a [robot engine](Robots "Robots") 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 [NUTN](https://en.wikipedia.org/wiki/National_University_of_Tainan) and [OPU](https://en.wikipedia.org/wiki/Osaka_Prefecture_University), for example, the number of [MCTS](Monte-Carlo_Tree_Search "Monte-Carlo Tree Search") 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 <a id="cite-note-3" href="#cite-ref-3">[3]</a>, 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.