Decision support of Bad Player Identification in MOBA Games using PageRank based Evidence Accumulation and Normal Distribution based Confidence Interval

Abstract

On-line game service is the hottest game genre today, and one of major on-line game is MOBA (multiplayer online battle arena) game. In MOBA games, the collaboration of team players and team strategy are vital elements together, besides the individual player's game control capability. Thus important issue for the MOBA service providers is to detect bad players showing abnormal plays or appearances in games with embedded malicious intentions. Previous approach had been presented to cope with such players; however they have not yet shown any promising results to judge each player. In this paper, we propose an efficient and automatic abnormal player decision support scheme using PageRank and normal distribution to find and judge bad players. Our scheme computes BPR (bad player ranking) for each user, and BPR can be used in service provider’s final decision to decide a specific user as an abnormal player. Our scheme has main advantage that it requires small computational efforts to identify bad players, because we utilize PageRank algorithm which shows efficient computation and information search capability.