Information extraction using 2 satisfiability based reverse analysis method in MOBA game

Abstract

League of Legends (LoL) has been on a rise in becoming an extremely favored multiplayer online battle arena (MOBA) game ever since its release in 2009. This paper presents a logic mining technique to model the results (Win / Lose) of the LoL games played in the professional league of South Korea, most commonly known as LCK. In this research, a method, namely k satisfiability based reverse analysis method (kSATRA) is brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games is used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams in LCK, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games is tested based on root mean square error (RMSE), mean absolute error (MAE) and CPU time. The main issue was to determine the relationship among gameplays or objectives and how they affect the outcome of the game. Results acquired from the computer simulation shows the effectiveness of kSATRA in exhibiting the performance of the LoL teams.