Behavior NPC Prediction Using Deep Learning

Adnan Maulana, S. Mardi, E. M. Yuniarno, Y. Suprapto
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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.
使用深度学习预测NPC行为
玩电子游戏是打发单调乏味的一天的一种方式。然而,很多人很快就厌倦了他们可以玩的一些游戏。这项研究将导致一种战略的发展,这种战略将在战斗中给予快速反应,并可能提高NPC的能力。这样做是为了避免这个问题。有一个非玩家角色被认为是游戏中最重要的角色之一(NPC)。具有自主性和适应性的npc可能会根据玩家的决定和环境条件改变自己的行为。以往的研究主要是利用神经网络方法来预测npc的行为;然而,这种方法有一个缺点,即预测的行为不一定如预期的那样,这导致了较差的准确性。本研究试图通过使用非玩家角色(NPC)的力量、玩家与NPC之间的距离以及对手的力量这三个输入参数来回答准确性不足的问题。测试结果表明,机器学习技术可以用于识别NPC行为分析的结果以及达到的准确性水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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