FML-based prediction agent and its application to game of Go

Abstract

Abstract-In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a 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 NUTN and OPU, for example, the number of 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, 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.

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

“…Figure 9 shows the accuracy of the proposed approach, indicating that FML-2/Method-2 demonstrated the optimal performance and that Method 2 is more efficient than Method 1. The difference between this work and a previous study 41 is that the overall game situation of Ref. 41 is acquired on the basis of a partial game situation (e.g., three or four subgames) as well as the results of the FML assessment engine and FML-based decision support engine.…”

“…41 is acquired on the basis of a partial game situation (e.g., three or four subgames) as well as the results of the FML assessment engine and FML-based decision support engine. 41 In the present study, the overall game situation is decided by the proposed methods listed in Table 11. Fig.…”