Penerapan Probabilistic FSM pada AI musuh dalam game ARPG untuk gerakan AI tidak monoton
Abstract
Game is really popular and becoming one of human aspects of life from child, Along with times the needs of game AI that’s not monotone has become more and more real, The problem that made a game monotone is the AI repeating its movement, with that the AI that doesn’t repeat its movement is made, this AI will make players not bored because of spam movement also motivated to play the game. Previous research has been made but with different genre and different random technic.
Game will be developed with Probabilistic finite state machine methods combine with random shuffle bag. Probability will be used for showing the animation so it will make the animation that’s come out more than one, and random shuffle bag will be used for the decision making for the movement so its evenly divided and not repeated.
Result of the testing shows that AI is not repeating the movement but it can repeat the pattern of the movement, Problem with repeating pattern can be solve with probability on animation that’s come out so with the same pattern movement player can see different movement.
References
[1] Ekawati Yulsilviana, & Hanifah Ekawati. 2020. Penerapan metode finite state machine (FSM) pada game agent legenda anak borneo. Sebatik, 23(1), 116–123. https://jurnal.wicida.ac.id/index.php/sebatik/article/view/453
[2] Kristo Radion Purba, Rini Nur Hasanah, & M. Aziz Muslim. 2013. Implementasi Logika Fuzzy Untuk Mengatur Perilaku Musuh dalam Game Bertipe Action-RPG. Jurnal EECCIS, 7(1), 15–20. https://jurnaleeccis.ub.ac.id/index.php/eeccis/article/view/196
[3] Lailiyah, S., Yunita, Y., Mallala, S., & Andrea, R. 2019. Probabilitas dalam Finite State Machine Agen Cerdas Edugame “Ajut-Ajut Kids.” Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 8(2), 151. https://doi.org/10.22146/jnteti.v8i2.504
[4] Marsaglia, G., & Zaman, A. 1991. A New Class of Random Number Generators. The Annals of Applied Probability, 1(3), 462–480. https://doi.org/10.1214/aoap/1177005878
[5] Probabilistic State Machines | AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors. 2017. Flylib.Com. https://flylib.com/books/en/2.71.1.298/1/
[6] Purba, K. R. 2016. Optimization of AI Tactic in Action-RPG Game. Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015), 131–137. https://doi.org/10.1007/978-981-287-988-2_14
[7] Simonov, A., Zagarskikh, A., & Fedorov, V. 2019. Applying Behavior characteristics to decision-making process to create believable game AI. Procedia Computer Science, 156, 404–413. https://doi.org/10.1016/j.procs.2019.08.222
[8] Tito Bimantoro, & Hanny Haryanto. (2016). Pemodelan Perilaku Musuh Menggunakan Finite State Machine (FSM) Pada Game Pengenalan Unsur Kimia. Journal of Applied Intelligent System, 1(3), 210–219. https://doi.org/10.33633/jais.v1i3.1254
[9] Yusnita, A., Sefty Wijayanti, & Putri Alysia Felita. 2017. Implementasi Algoritma Shuffle Random Pada Edugame Magic Time Berbasis Universal Windows Platform (UWP). Prosiding SNITT POLTEKBA, 2(1), 203–208. https://jurnal.poltekba.ac.id/index.php/prosiding/article/view/399
