Analysis of methods for simulating character encounters in a game  with RPG elements

Michał Zdybel; Jakub Smołka

doi:10.35784/jcsi.8353

Open full text

PDF

Published: Dec 30, 2025

DOI: https://doi.org/10.35784/jcsi.8353

Issue Vol. 37 (2025)

Articles

Performance evaluation of Machine Learning and Deep Learning models for 5G resource allocation
Abdullah Havolli, Majlinda Fetaji

371-378
Analysis of the use of object detection systems in edge computing
Jakub Kozłowski, Marcin Badurowicz

379-390
Performance analysis of Jetpack Compose components in mobile applications
Adrian Kwiatkowski, Jakub Smołka

391-398
Methods for comparing three-dimensional motion trajectories
Tomasz Waldemar Samorow, Maria Skublewska-Paszkowska

399-404
Performance and scalability analysis of monolithic and microservice architectures in social networks
Viacheslav Chernohor

405-409
Comparative analysis of methods for identifying tree structures of coronary vessels
Kacper Liżewski, Małgorzata Charytanowicz

410-417
Websites accessibility assessment of voivodeship cities in Poland
Kacper Czajka, Maria Skublewska-Paszkowska

418-425
Analysis of ORM framework approaches for Node.js
Serhii Zhadko-Bazilevych

426-430
Analysis of performance optimization methods for 3D games in the Unity environment
Maciej Potręć, Marcin Badurowicz

431-435
The impact of AI use on the performance of chess engines
Jakub Król, Jakub Smołka

436-442
Evaluating the effectiveness of selected tools in recognizing emotions from facial photos
Klaudiusz Wierzbowski

443-450
Performance analysis of the GraphQL API creation technologies using Spring Boot and NestJS
Jakub Maciej Tkaczyk, Beata Pańczyk

451-456
Comparative Performance Analysis of RabbitMQ and Kafka Message Queue Systems in Spring Boot and ASP.NET Environments
Filip Kamiński, Radosław Kłonica, Beata Pańczyk

457-462
Analysis of current threats and security measures used in web applications on the example of Symfony, Express, and Spring Boot
Karol Kurowski, Magdalena Kramek

463-469
The use of machine learning to classify symbols on cultural monuments to identify their origin and historical period.
Igor Pajura, Sylwester Korga

470-475
Investigating Machine Learning Algorithms for Stroke Occurrence Prediction
Kazeem B. Adedeji, Titilayo A. Ogunjobi, Thabane H. Shabangu, Joshua A. Omowaye

476-483
Comparative performance analysis of Spring Boot and Quarkus frameworks in Java applications
Grzegorz Szymanek, Jakub Smołka

484-491
Influence of activation function in deep learning for cutaneous melanoma identification
Adrian Szymczyk, Maria Skublewska-Paszkowska

492-499
Analysis of methods for simulating character encounters in a game with RPG elements
Michał Zdybel, Jakub Smołka

500-507
Analysis of the efficiency of Apex and Java languages and related technologies in performing database operations
Marcin Janczarek, Konrad Lewicki, Jakub Smołka

508-514

DOI

https://doi.org/10.35784/jcsi.8353

Authors

Michał Zdybel

s95620@pollub.edu.pl

Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

https://orcid.org/0009-0002-2536-9229

Jakub Smołka

jakub.smolka@pollub.pl

Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

https://orcid.org/0000-0002-8350-2537

Abstract

This paper investigates algorithms that predict the outcome of a duel in a game with RPG elements and determine the losses incurred. The aim is to evaluate the effectiveness of the following approaches: based on Lanchester's laws and stochastic, using the Monte Carlo method. Verification was carried out through manual gameplay and comparison of the obtained results with those predicted by the algorithms, measuring their accuracy with the MAPE. The analysis showed greater efficiency and stability of the Monte Carlo algorithm, while the Lanchester model turned out to be less reliable in one of the cases.

Keywords:

algorithms, Lanchester's laws, Monte Carlo, games with RPG elements, quick combat

References

[1] R. Hoffmann, T. Protasowicki, Modelowanie pola walki z zastosowaniem koncepcji dynamiki systemowej, Biuletyn Instytutu Systemów Informatycznych 12 (2013) 29-34.

[2] M. Kress, Lanchester models for irregular warfare, Mathematics 8(5) (2020) 737, https://doi.org/10.3390/math8050737.

[3] N. J. MacKay, Lanchester combat models, Mathematics Today 42 (2006) 170-173, https://doi.org/10.48550/arXiv.math/0606300.

[4] K. Y. Lin, N. J. MacKay, The optimal policy for the one-against-many heterogeneous Lanchester model, Operations Research Letters 42(6-7) (2014) 473-477, https://doi.org/10.1016/j.orl.2014.08.008.

[5] K. Y. Lin, New results on a stochastic duel game with each force consisting of heterogeneous units, Naval Research Logistics (NRL) 61(1) (2014) 56-65, https://doi.org/10.1002/nav.21566.

[6] N. J. MacKay, Lanchester models for mixed forces with semi-dynamical target allocation, Journal of the Operational Research Society 60(10) (2009) 1421-1427, https://doi.org/10.1057/jors.2008.97.

[7] S. G. Coulson, Lanchester modelling of intelligence in combat, IMA Journal of Management Mathematics 30(2) (2019) 149-164, https://doi.org/10.1093/imaman/dpx014.

[8] M. J. Kearney, R. J. Martin, On a stochastic version of Lanchester’s model of combat, arXiv preprint arXiv:1905.03122 (2019), https://doi.org/10.48550/arXiv.1905.03122.

[9] M. Kostić, A. Jovanović, Lanchester’s differential equations as operational command decision making tools, Serbian Journal of Management 18(1) (2023) 71-92, http://dx.doi.org/10.5937/sjm18-39699.

[10] O. Batarseh, D. Singham, Interval-based simulation to model input uncertainty in stochastic Lanchester models, Military Operations Research 18(1) (2013) 61–75, http://www.jstor.org/stable/24838472.

[11] D. P. Kroese, R. Y. Rubinstein, Monte Carlo methods, WIREs Computational Statistics 4(1) (2012) 48–58, https://doi.org/10.1002/wics.194.

[12] M. McCartney, The solution of Lanchester’s equations with inter-battle reinforcement strategies, Physica A: Statistical Mechanics and its Applications 586 (2022) 126477, https://doi.org/10.1016/j.physa.2021.126477.

[13] Ł. Gałka, P. Karczmarek, D. Czerwiński, Application of Monte Carlo algorithms with neural network-based intermediate area to the Thousand Card Game, In: L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, J. M. Zurada, (eds) Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, Springer 14125 (2023) 68-77, https://doi.org/10.1007/978-3-031-42505-9_7.

[14] G. E. M. Long, D. Perez-Liebana, S. Samothrakis, Balancing Wargames through predicting unit point costs, In 2023 IEEE Conference on Games (CoG) (2023) 1–8, https://doi.org/10.1109/CoG57401.2023.10333152.

[15] M. Flanagan, D. Lambert, T. C. Lipscombe, A. Northey, I. M. Robinson, Lanchester’s fighting strength as a battle outcome predictor applied to a simple fire and manoeuvre wargame, In Recent Advances in Monte Carlo Methods, IntechOpen (2024), https://doi.org/10.5772/intechopen.1002384.

Zdybel, M., & Smołka, J. (2025). Analysis of methods for simulating character encounters in a game with RPG elements. Journal of Computer Sciences Institute, 37, 500–507. https://doi.org/10.35784/jcsi.8353

Analysis of methods for simulating character encounters in a game with RPG elements

Issue Vol. 37 (2025)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 37 (2025)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License