Adisusilo Anang Kukuh, Hariadi Mochamad, Yuniarno Eko Mulyanto, Purwantana Bambang
Departement of Informatics Engineering, Faculty of Engineering, University of Wijaya Kusuma Surabaya, Jl. Dukuh Kupang XXV/54, Dukuh Kupang, Dukuh Pakis, Surabaya, East Jawa 60225, Indonesia.
Department of Computer Engineering, Faculty of Electrical Technology, Institut Technology of Sepuluh November Surabaya, 60111, Indonesia.
Heliyon. 2020 Mar 28;6(3):e03613. doi: 10.1016/j.heliyon.2020.e03613. eCollection 2020 Mar.
In most cases, problems that increase player involvement in immersive serious games do so by combining fun elements with a specific purpose. Previous studies have produced models of soil porosity and plow force that use the speed of plowing, the angle of the plow's eye, and the depth of the plow as the basis for a design strategy in immersion serious games. However, these studies have not been able to show the optimal strategy of engagement of the player in the game. In the domain of serious game concept learning, strategies can be formed based on real conditions or data from experimental results. In a serious game, the aim is to increase the player's knowledge so that the player gains knowledge by coming up with strategies to play the game. This research aims to increase the engagement of players by means of multi-objective optimization based on Pareto optima, with the objectivity of soil porosity and plow force that is affected by the speed of plowing, the angle of the plow's eye, and the depth of the plow. The results of this optimization are used as a basis for the design of strategies in a serious game in the form of Hierarchy Finite State Machine (HFSM). From the results of the study, it was found that there is an optimal area for the game strategy that is also an indicator of how to successfully process the soil tillage using a moldboard plow.
在大多数情况下，增加玩家对沉浸式严肃游戏参与度的问题是通过将有趣元素与特定目的相结合来实现的。先前的研究已经建立了土壤孔隙率和犁耕力模型，这些模型将犁耕速度、犁铧角度和犁耕深度作为沉浸式严肃游戏设计策略的基础。然而，这些研究未能展示玩家在游戏中的最佳参与策略。在严肃游戏概念学习领域，可以根据实际情况或实验结果数据来形成策略。在严肃游戏中，目标是增加玩家的知识，使玩家通过想出玩游戏的策略来获取知识。本研究旨在通过基于帕累托最优的多目标优化来提高玩家的参与度，目标是受犁耕速度、犁铧角度和犁耕深度影响的土壤孔隙率和犁耕力。这种优化的结果被用作以层次有限状态机（HFSM）形式设计严肃游戏策略的基础。从研究结果中发现，游戏策略存在一个最佳区域，这也是使用铧式犁成功进行土壤耕作的指标。
