Multi-agent system for a semi-realistic simulation of animal behavior in an educational game about ecosystems
Resumo
Introduction: Educational games can support environmental education by allowing players to explore ecosystems interactively, yet animal behavior in these games remains underexplored, particularly regarding responses to environmental conditions and survival needs. Objective: This work presents a multi-agent system for semi-realistic animal simulation in GAIA, an educational game about Brazilian ecosystems, assessing agents’ survival behaviors and players’ perceptions. Methodology: The system was developed in Unity using C#, Finite State Machines, pluggable behaviors, Unity Navigation, and A* pathfinding. Agents make decisions based on spatial perception, time cycles, and internal needs (hunger, thirst, energy). Evaluation included simulation trials and a post-test questionnaire with 18 participants. Results: The agents portraying the four depicted species survived more effectively than a baseline agent, with statistically significant differences between the four species and the baseline. Participants reported positive perceptions of the animals’ behavior and its contribution to the game experience, indicating that the proposed system is a promising foundation for semi-realistic animal behavior in educational ecosystem games.
Palavras-chave:
Multi-agent systems, Artificial Intelligence, Ecosystem simulation, Educational games, Serious games
Referências
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Wangersky, P. J. (1978). Lotka-volterra population models. Annual Review of Ecology and Systematics, 9:189–218.
Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
Wooldridge, M. e Jennings, N. R. (1995). Intelligent agents: Theory and practice. The knowledge engineering review, 10(2):115–152.
Boncu, S., Candel, O.-S., e Popa, N. L. (2022). Gameful green: a systematic review on the use of serious computer games and gamified mobile apps to foster pro-environmental information, attitudes and behaviors. Sustainability, 14(16):10400.
Cui, X. e Shi, H. (2011). A*-based pathfinding in modern computer games. International Journal of Computer Science and Network Security, 11(1):125–130.
Davies, N. B., Krebs, J. R., e West, S. A. (2012). An introduction to behavioural ecology. John Wiley & Sons.
Dawkins, M. S. (2007). Observing animal behaviour: design and analysis of quantitative data. Oxford University Press.
Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3):241–252.
Ferreira Bezerra, L., Tórtoro Pereira, L., Oliveira, W., Hamari, J., e Mazzoni Ranieri, C. (2025). Sustainable planet-mission preserve: A serious game for solid waste management awareness. In Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play, pages 169–176.
Grelle, C. E., Bayma, A. P., Paixão, L. R. L., Egler, M., Senta, M. M. D., Jenkins, C. N., Uezu, A., Pellin, A., Martensen, A. C., Shirai, H., et al. (2021). Conservation initiatives in the brazilian atlantic forest. In The Atlantic Forest: history, biodiversity, threats and opportunities of the mega-diverse forest, pages 421–449. Springer.
Joshi, A., Kale, S., Chandel, S., e Pal, D. K. (2015). Likert scale: Explored and explained. British journal of applied science & technology, 7(4):396–403.
Klei Entertainment (2013). Don’t Starve. Available at: [link]. Accessed on: April 30, 2026.
Kruskal, W. H. e Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American statistical Association, 47(260):583–621.
Kung, F. Y., Kwok, N., e Brown, D. J. (2018). Are attention check questions a threat to scale validity? Applied Psychology, 67(2):264–283.
Lewinsohn, T. M. e Prado, P. I. (2005). Quantas espécies há no brasil. Megadiversidade, 1(1):36–42.
Luccas, M. d. S. e Branco, K. R. L. J. C. (2023). Star owners: um modelo construtivista de desenvolvimento de serious games para jogo de ensino de teoria da computaçao. Anais.
Marques, M. C. e Grelle, C. E. (2021). The atlantic forest. History, Biodiversity, Threats and Opportunities of the Mega-Diverse Forest; Springer International Publishing: Cham, Switzerland, pages 3–24.
Maxis (2014). The sims 4. Available at: [link]. Accessed on: April 30, 2026.
Metcalf, S. J., Gagnon, D., e Slater, S. (2023). Shifts in student attitudes and beliefs about science through extended play in an immersive science game. In International conference on immersive learning, pages 330–342. Springer.
Mojang Studios (2009). Minecraft. Available at: [link]. Accessed on: April 30, 2026.
Pei, W. et al. (2020). Attention please: Your attention check questions in survey studies can be automatically answered. In Proceedings of The Web Conference 2020, page 1182–1193. Association for Computing Machinery.
Shapiro, S. S. e Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4):591–611.
Silber, H., Roßmann, J., e Gummer, T. (2022). The issue of noncompliance in attention check questions: False positives in instructed response items. Field Methods, 34(4):346–360.
Strannegård, C., Engsner, N., Eisfeldt, J., Endler, J., Hansson, A., Lindgren, R., Mostad, P., Olsson, S., Perini, I., Reese, H., et al. (2022). Ecosystem models based on artificial intelligence. In 2022 Swedish Artificial Intelligence Society Workshop (SAIS), pages 1–9. IEEE.
Sunehag, P., Lever, G., Liu, S., Merel, J., Heess, N., Leibo, J. Z., Hughes, E., Eccles, T., e Graepel, T. (2019). Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems. In Artificial life conference proceedings, pages 103–110. MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info.
Turan, E. e Çetin, G. (2019). Using artificial intelligence for modeling of the realistic animal behaviors in a virtual island. Computer Standards & Interfaces, 66:103361.
Veronica, R. e Calvano, G. (2020). Promoting sustainable behavior using serious games: Seadventure for ocean literacy. IEEE Access, 8:196931–196939.
Viana, B. M., Pereira, L. T., e Toledo, C. F. (2022). Illuminating the space of enemies through map-elites. In 2022 IEEE Conference on Games (CoG), pages 17–24. IEEE.
Wangersky, P. J. (1978). Lotka-volterra population models. Annual Review of Ecology and Systematics, 9:189–218.
Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
Wooldridge, M. e Jennings, N. R. (1995). Intelligent agents: Theory and practice. The knowledge engineering review, 10(2):115–152.
Publicado
29/09/2026
Como Citar
LEITE, André Castilho; KIEL, Kaio Henrique Gava; THICHAKI, Lucas Evangelista; GARCIA, Maria Caroline Alves; RANIERI, Caetano Mazzoni; RIBEIRO, Milton Cezar; PEREIRA, Leonardo Tórtoro.
Multi-agent system for a semi-realistic simulation of animal behavior in an educational game about ecosystems. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1016-1027.
DOI: https://doi.org/10.5753/sbgames.2026.26370.
