Adnan Maulana, S. Mardi, E. M. Yuniarno, Y. Suprapto
{"title":"Behavior NPC Prediction Using Deep Learning","authors":"Adnan Maulana, S. Mardi, E. M. Yuniarno, Y. Suprapto","doi":"10.1109/CENIM56801.2022.10037328","DOIUrl":null,"url":null,"abstract":"Playing video games is one way to break up the monotony of an otherwise boring day. However, a significant number of people quickly tire of playing some of the games that are available to them. This study will result in the development of a strategy that will give a rapid response during combat and may boost NPC abilities. This will be done so that this problem can be circumvented. There is a non-player character that is considered to be one of the most important in the game (NPC). NPCs that are autonomous and adaptive may change their behavior in reaction to the decisions made by the player as well as the conditions in their environment. Previous research has made use of the neural network methodology to forecast the behavior of NPCs; however, this method has a drawback in that the predicted behavior is not necessarily as desired, which leads to a poor level of accuracy. This study attempts to answer the problem of inadequate accuracy by using as its three input parameters the power of the non-player character (NPC), the distance between the player and the NPC, and the power of the opponent. The outcomes of the test indicate that the machine learning technique may be used to identify the results of the NPC behavior analysis as well as the level of accuracy reached.","PeriodicalId":118934,"journal":{"name":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CENIM56801.2022.10037328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Playing video games is one way to break up the monotony of an otherwise boring day. However, a significant number of people quickly tire of playing some of the games that are available to them. This study will result in the development of a strategy that will give a rapid response during combat and may boost NPC abilities. This will be done so that this problem can be circumvented. There is a non-player character that is considered to be one of the most important in the game (NPC). NPCs that are autonomous and adaptive may change their behavior in reaction to the decisions made by the player as well as the conditions in their environment. Previous research has made use of the neural network methodology to forecast the behavior of NPCs; however, this method has a drawback in that the predicted behavior is not necessarily as desired, which leads to a poor level of accuracy. This study attempts to answer the problem of inadequate accuracy by using as its three input parameters the power of the non-player character (NPC), the distance between the player and the NPC, and the power of the opponent. The outcomes of the test indicate that the machine learning technique may be used to identify the results of the NPC behavior analysis as well as the level of accuracy reached.