Técnicas de Inteligência Artificial na Criação de Personagens Não Jogáveis: uma Revisão de Literatura
Resumo
Os NPCs (Non-Player Character), personagens não jogáveis, nem sempre possuem comportamentos responsivos. E, está artificialidade ou até anomalia pode gerar imprevistos ou problemas de qualidade em um jogo causando até o desinteresse do jogador. Com base nesta afirmação, este trabalho apresenta uma Revisão sistemática de Literatura (RSL) sobre o desenvolvimento de NPCs com comportamentos engajados analisando as principais técnicas de Inteligência Artificial que podem contribuir para a resolução deste problema. A revisão possibilitou o mapeamento e o conhecimento do estado atual dos estudos correlatos, extraindo 32 artigos relacionados ao estado da arte. A partir da análise, verificou-se que a técnica intitulada GOAP (Goal Oriented Action Planning) pode ser uma alternativa na geração de comportamento de NPCs mais engajados em um ambiente virtual.
Palavras-chave:
Jogos Digitais, NPC (Personagens não jogáveis), Revisão Sistemática
Referências
Addoum, M. A., Mekhaemar, J., Rouffet, M., and Jacopin, (2021a). Khaldun: Goap for both procedural level generation and npc behaviors. In 2021 IEEE Conference on Games (CoG), pages 1–2.
Addoum, M. A., Rouffet, M., and Jacopin, (2021b). 3d brawler game using a hybrid planning approach. In 2021 IEEE Conference on Games (CoG), pages 1–2.
Agliata, F., Bertoli, M., Ripamonti, L., Maggiorini, D., and Gadia, D. (2019). Adding variety in npcs behaviour using emotional states and genetic algorithms: the genie project. In International Conference on Intelligent Games and Simulation, pages 45– 49. EUROSIS.
Baffa, A., Sampaio, P., Feijó, B., and Lana, M. (2017). Dealing with the emotions of non player characters. In 2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 76–87. IEEE.
Bailey, C. and Katchabaw, M. (2008). An emergent framework for realistic psychosocial behaviour in non player characters. In Proceedings of the 2008 Conference on Future Play: Research, Play, Share, pages 17–24.
Chen, T., Richoux, F., Torres, J. M., and Inoue, K. (2021). Interpretable utility-based models applied to the fightingice platform. In 2021 IEEE Conference on Games (CoG), pages 1–8.
da Silva, G. A. and de Souza Ribeiro, M. W. (2021a). Desenvolvimento de npcs com comportamentos engajados.
da Silva, G. A. and de Souza Ribeiro, M. W. (2021b). Development of non-player character with believable behavior: a systematic literature review.
Dustin D. Updyke, Thomas G. Podnar, G. B. D. J. W. Y. (2021). Using machine learning to increase npc fidelity.
Frosi, F. O. and da Silva, I. C. S. (2019). Building bots for shooter games based on the bartle’s player types and finite state machines: A battling behaviour analysis.
Guerrero-Romero, C. and Perez-Liebana, D. (2021). Map-elites to generate a team of agents that elicits diverse automated gameplay. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Hamdy, S. and King, D. (2017). Affect and believability in game characters–a review of the use of affective computing in games. In Proceedings of the 18th Annual Conference on Simulation and AI in Computer Games. EUROSIS.
Johansson, A. and Dell’Acqua, P. (2012a). Comparing behavior trees and emotional behavior networks for npcs. In 2012 17th International Conference on Computer Games (CGAMES), pages 253–260. IEEE.
Johansson, A. and Dell’Acqua, P. (2012b). Emotional behavior trees. In 2012 IEEE Conference on Computational Intelligence and Games (CIG), pages 355–362. IEEE.
King, D. J. and Bennett, C. (2016). An investigation of two real time machine learning techniques that could enhance the adaptability of game ai agents. In GAMEON’2016: 17th International Conference on Intelligent Games and Simulation, pages 41–48. EUROSIS.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Lazarin, L. V. and Cherobin, R. (2017). A relação entre o processo de tomada de decisão e level design. In Proceedings of the XVI Brazilian Symposium on Computer Games and Digital Entertainment, pages 1264–1267.
Lemaitre, J., Lourdeaux, D., and Chopinaud, C. (2015). Towards a resource-based model of strategy to help designing opponent ai in rts games. In 7th international conference on agents and artificial intelligence (icaart 2015), volume 1, pages 210–215.
Müller-Brockhausen, M., Preuss, M., and Plaat, A. (2021). A new challenge: Approaching tetris link with ai. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Pfau, J., Smeddinck, J. D., and Malaka, R. (2020). The case for usable ai: What industry professionals make of academic ai in video games. In Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play, pages 330–334.
Sestini, A., Kuhnle, A., and Bagdanov, A. D. (2021). Policy fusion for adaptive and customizable reinforcement learning agents. In 2021 IEEE Conference on Games (CoG), pages 01–08.
Thaís Ferreira, Esteban Clua, T. C. K. (2021). Centralized critic per knowledge for cooperative multi-agent game environments. In SBGames 2021, pages 1–10.
Tutum, C., AbdulQuddos, S., and Miikkulainen, R. (2021). Generalization of agent behavior through explicit representation of context. In 2021 IEEE Conference on Games (CoG), pages 1–7.
Yuda, K., Mozgovoy, M., and Danielewicz-Betz, A. (2019). Creating an affective fighting game ai system with gamygdala. In 2019 IEEE Conference on Games (CoG), pages 1–4. IEEE.
Addoum, M. A., Rouffet, M., and Jacopin, (2021b). 3d brawler game using a hybrid planning approach. In 2021 IEEE Conference on Games (CoG), pages 1–2.
Agliata, F., Bertoli, M., Ripamonti, L., Maggiorini, D., and Gadia, D. (2019). Adding variety in npcs behaviour using emotional states and genetic algorithms: the genie project. In International Conference on Intelligent Games and Simulation, pages 45– 49. EUROSIS.
Baffa, A., Sampaio, P., Feijó, B., and Lana, M. (2017). Dealing with the emotions of non player characters. In 2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 76–87. IEEE.
Bailey, C. and Katchabaw, M. (2008). An emergent framework for realistic psychosocial behaviour in non player characters. In Proceedings of the 2008 Conference on Future Play: Research, Play, Share, pages 17–24.
Chen, T., Richoux, F., Torres, J. M., and Inoue, K. (2021). Interpretable utility-based models applied to the fightingice platform. In 2021 IEEE Conference on Games (CoG), pages 1–8.
da Silva, G. A. and de Souza Ribeiro, M. W. (2021a). Desenvolvimento de npcs com comportamentos engajados.
da Silva, G. A. and de Souza Ribeiro, M. W. (2021b). Development of non-player character with believable behavior: a systematic literature review.
Dustin D. Updyke, Thomas G. Podnar, G. B. D. J. W. Y. (2021). Using machine learning to increase npc fidelity.
Frosi, F. O. and da Silva, I. C. S. (2019). Building bots for shooter games based on the bartle’s player types and finite state machines: A battling behaviour analysis.
Guerrero-Romero, C. and Perez-Liebana, D. (2021). Map-elites to generate a team of agents that elicits diverse automated gameplay. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Hamdy, S. and King, D. (2017). Affect and believability in game characters–a review of the use of affective computing in games. In Proceedings of the 18th Annual Conference on Simulation and AI in Computer Games. EUROSIS.
Johansson, A. and Dell’Acqua, P. (2012a). Comparing behavior trees and emotional behavior networks for npcs. In 2012 17th International Conference on Computer Games (CGAMES), pages 253–260. IEEE.
Johansson, A. and Dell’Acqua, P. (2012b). Emotional behavior trees. In 2012 IEEE Conference on Computational Intelligence and Games (CIG), pages 355–362. IEEE.
King, D. J. and Bennett, C. (2016). An investigation of two real time machine learning techniques that could enhance the adaptability of game ai agents. In GAMEON’2016: 17th International Conference on Intelligent Games and Simulation, pages 41–48. EUROSIS.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Lazarin, L. V. and Cherobin, R. (2017). A relação entre o processo de tomada de decisão e level design. In Proceedings of the XVI Brazilian Symposium on Computer Games and Digital Entertainment, pages 1264–1267.
Lemaitre, J., Lourdeaux, D., and Chopinaud, C. (2015). Towards a resource-based model of strategy to help designing opponent ai in rts games. In 7th international conference on agents and artificial intelligence (icaart 2015), volume 1, pages 210–215.
Müller-Brockhausen, M., Preuss, M., and Plaat, A. (2021). A new challenge: Approaching tetris link with ai. In 2021 IEEE Conference on Games (CoG), pages 1–8.
Pfau, J., Smeddinck, J. D., and Malaka, R. (2020). The case for usable ai: What industry professionals make of academic ai in video games. In Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play, pages 330–334.
Sestini, A., Kuhnle, A., and Bagdanov, A. D. (2021). Policy fusion for adaptive and customizable reinforcement learning agents. In 2021 IEEE Conference on Games (CoG), pages 01–08.
Thaís Ferreira, Esteban Clua, T. C. K. (2021). Centralized critic per knowledge for cooperative multi-agent game environments. In SBGames 2021, pages 1–10.
Tutum, C., AbdulQuddos, S., and Miikkulainen, R. (2021). Generalization of agent behavior through explicit representation of context. In 2021 IEEE Conference on Games (CoG), pages 1–7.
Yuda, K., Mozgovoy, M., and Danielewicz-Betz, A. (2019). Creating an affective fighting game ai system with gamygdala. In 2019 IEEE Conference on Games (CoG), pages 1–4. IEEE.
Publicado
24/10/2022
Como Citar
VALADARES, Gabriel Pacini O.; RIBEIRO, Marcos Wagner S..
Técnicas de Inteligência Artificial na Criação de Personagens Não Jogáveis: uma Revisão de Literatura. In: TRILHA DE ARTES & DESIGN – ARTIGOS COMPLETOS - SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 21. , 2022, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 208-217.
DOI: https://doi.org/10.5753/sbgames_estendido.2022.226106.
