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This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games.
L. Ferreira and C. Toledo, “A search-based approach for generating angry birds levels,” in Proceedings of the 9th IEEE International Conference on Computational Intelligence in Games , ser. CIG’14, 2014
2014
Earlier work this paper cites.
N. Shaker, J. Togelius, and M. J. Nelson, Procedural content generation in games . Springer, 2016
2016
Earlier work this paper cites.
M. Kaidan, T. Harada, C. Y. Chu, and R. Thawonmas, “Procedural generation of angry birds levels with adjustable difficulty,” in 2016 IEEE Congress on Evolutionary Computation (CEC) . IEEE, 2016, pp. 1311–1316
2016
Earlier work this paper cites.
M. Stephenson and J. Renz, “Generating varied, stable and solvable levels for angry birds style physics games,” in 2017 IEEE Conference on Computational Intelligence and Games (CIG) , 2017, pp. 288–295
2017
Earlier work this paper cites.
Y. Jiang, T. Harada, and R. Thawonmas, “Procedural generation of angry birds fun levels using pattern-struct and preset-model,” in 2017 IEEE Conference on Computational Intelligence and Games (CIG) , 2017, pp. 154–161
2017
Earlier work this paper cites.
D. Gravina, A. Khalifa, A. Liapis, J. Togelius, and G. N. Yannakakis, “Procedural content generation through quality diversity,” in 2019 IEEE Conference on Games (CoG) . IEEE, 2019, pp. 1–8
2019
Earlier work this paper cites.
F. Abdullah, P. Paliyawan, R. Thawonmas, T. Harada, and F. A. Bachtiar, “An angry birds level generator with rube goldberg machine mechanisms,” in 2019 IEEE Conference on Games (CoG) . IEEE, 2019, pp. 1–8
2019
Earlier work this paper cites.
T. B. Brown et al. , “Language models are few-shot learners,” 2020
2020
Earlier work this paper cites.
F. Abdullah, P. Paliyawan, R. Thawonmas, and F. A. Bachtiar, “Generating angry birds-like levels with domino effects using constrained novelty search,” in 2020 IEEE Conference on Games (CoG) . IEEE, 2020, pp. 698–701
2020
Earlier work this paper cites.
C. Gamage, V. Pinto, J. Renz, and M. Stephenson, “Deceptive level generation for angry birds,” in 2021 IEEE Conference on Games (CoG) , 2021, pp. 1–8
2021
Earlier work this paper cites.
T. Tanabe, K. Fukuchi, J. Sakuma, and Y. Akimoto, “Level generation for angry birds with sequential vae and latent variable evolution,” in Proceedings of the Genetic and Evolutionary Computation Conference , ser. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 1052–1060. [Online]. Available: https://doi.org/10.1145/3449639.3459290
2021
Earlier work this paper cites.
A. Dosovitskiy et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” 2021
2021
Cited alongside, same era.
OpenAI, Nov 2022. [Online]. Available: https://openai.com/blog/chatgpt
2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin et al. , “Training language models to follow instructions with human feedback,” 2022
2022
Cited alongside, same era.
J. W. Rae et al. , “Scaling language models: Methods, analysis & insights from training gopher,” 2022
2022
Cited alongside, same era.
S. Smith et al. , “Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model,” 2022
2022
Cited alongside, same era.
E. N. Naumova, “A mistake-find exercise: a teacher’s tool to engage with information innovations, chatgpt, and their analogs,” Journal of Public Health Policy , Mar 2023. [Online]. Available: https://doi.org/10.1057/s41271-023-00400-1
2023
Closest in time.
M. Dowling and B. Lucey, “Chatgpt for (finance) research: The bananarama conjecture,” Finance Research Letters , vol. 53, p. 103662, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1544612323000363
2023
Closest in time.
S. Vemprala, R. Bonatti, A. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” Microsoft, Tech. Rep. MSR-TR-2023-8, February 2023. [Online]. Available: https://www.microsoft.com/en-us/research/publication/chatgpt-for-robotics-design-principles-and-model-abilities/
2023
Closest in time.
G. Todd, S. Earle, M. U. Nasir, M. C. Green, and J. Togelius, “Level generation through large language models,” 2023
2023
Closest in time.
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2022
Cited alongside, same era.
J. We et al. , “Emergent abilities of large language models,” Transactions on Machine Learning Research , 2022, survey Certification. [Online]. Available: https://openreview.net/forum?id=yzkSU5zdwD
2022
Cited alongside, same era.
E. Saravia, “Prompt Engineering Guide,” https://github.com/dair-ai/Prompt-Engineering-Guide , 12 2022
2022
Cited alongside, same era.
S. Min et al. , “Rethinking the role of demonstrations: What makes in-context learning work?” 2022
2022
Cited alongside, same era.
K. Hu, “Chatgpt sets record for fastest-growing user base - analyst note,” Feb 2023. [Online]. Available: https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
2023
Cited alongside, same era.
A. Gilson et al. , “How does chatgpt perform on the united states medical licensing examination? the implications of large language models for medical education and knowledge assessment,” JMIR Med Educ , vol. 9, p. e45312, Feb 2023. [Online]. Available: http://www.ncbi.nlm.nih.gov/pubmed/36753318
2023
Cited alongside, same era.
OpenAI. [Online]. Available: https://platform.openai.com/docs/models/gpt-3-5
Cited in the paper.
S. Sudhakaran, M. González-Duque, C. Glanois, M. Freiberger, E. Najarro, and S. Risi, “Mariogpt: Open-ended text2level generation through large language models,” 2023
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, jan 2023. [Online]. Available: https://doi.org/10.1145/3560815
2023
Closest in time.
J. White et al. , “A prompt pattern catalog to enhance prompt engineering with chatgpt,” 2023
2023
Closest in time.
J. Wei et al. , “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
Closest in time.
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” 2023
2023
Closest in time.
Y. Zhou et al. , “Large language models are human-level prompt engineers,” 2023
2023
Closest in time.