Fetching the paper…
Reading the bibliography…
We study the problem of efficiently generating high-quality and diverse content in games.
“Magic the gathering,” Wizards of the Coast, 1993. [Online]. Available: https://magic.wizards.com/en
1993
Earlier work this paper cites.
C. D. Ward and P. I. Cowling, “Monte carlo search applied to card selection in magic: The gathering,” in 2009 IEEE Symposium on Computational Intelligence and Games . IEEE, 2009, pp. 9–16
2009
Earlier work this paper cites.
Y. Zhang, R. Jin, and Z.-H. Zhou, “Understanding bag-of-words model: a statistical framework,” International Journal of Machine Learning and Cybernetics , vol. 1, no. 1-4, pp. 43–52, 2010
2010
Earlier work this paper cites.
J. Lehman and K. O. Stanley, “Abandoning objectives: Evolution through the search for novelty alone,” Evolutionary computation , vol. 19, no. 2, pp. 189–223, 2011
2011
Earlier work this paper cites.
J. Lehman and K. O. Stanley, “Evolving a diversity of virtual creatures through novelty search and local competition,” in Proceedings of the 13th annual conference on Genetic and evolutionary computation , 2011, pp. 211–218
2011
Earlier work this paper cites.
“Hearthstone,” Blizzard Entertainment, 2014. [Online]. Available: https://playhearthstone.com/en-us
2014
Earlier work this paper cites.
D. B. D’Ambrosio, J. Gauci, and K. O. Stanley, “Hyperneat: The first five years,” Growing adaptive machines , pp. 159–185, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J.-B. Mouret and J. Clune, “Illuminating search spaces by mapping elites,” 2015
2015
Earlier work this paper cites.
A. Cully, J. Clune, D. Tarapore, and J.-B. Mouret, “Robots that can adapt like animals,” Nature , vol. 521, no. 7553, p. 503–507, May 2015. [Online]. Available: http://dx.doi.org/10.1038/nature14422
2015
Earlier work this paper cites.
J. K. Pugh, L. B. Soros, P. A. Szerlip, and K. O. Stanley, “Confronting the challenge of quality diversity,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation , ser. GECCO ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 967–974. [Online]. Available: https://doi.org/10.1145/2739480.2754664
2015
Earlier work this paper cites.
J. Lehman and R. Miikkulainen, “Enhancing divergent search through extinction events,” in Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation , 2015, pp. 951–958
2015
Earlier work this paper cites.
P. García-Sánchez, A. Tonda, G. Squillero, A. Mora, and J. J. Merelo, “Evolutionary deckbuilding in hearthstone,” in 2016 IEEE Conference on Computational Intelligence and Games (CIG) . IEEE, 2016, pp. 1–8
2016
Earlier work this paper cites.
A. Stiegler, C. Messerschmidt, J. Maucher, and K. Dahal, “Hearthstone deck-construction with a utility system,” in 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA) . IEEE, 2016, pp. 21–28
2016
Earlier work this paper cites.
J. Pugh, L. Soros, and K. Stanley, “Quality diversity: A new frontier for evolutionary computation,” Frontiers in Robotics and AI , vol. 3, 07 2016
2016
Earlier work this paper cites.
T. Bartz-Beielstein, “A survey of model-based methods for global optimization,” 01 2016, pp. 1–
2016
Earlier work this paper cites.
N. Hansen, “The cma evolution strategy: A tutorial,” 2016
2016
Earlier work this paper cites.
N. Heijne and S. Bakkes, “Procedural zelda: A pcg environment for player experience research,” in Proceedings of the 12th International Conference on the Foundations of Digital Games , ser. FDG ’17. New York, NY, USA: Association for Computing Machinery, 2017. [Online]. Available: https://doi.org/10.1145/3102071.3102091
2017
Earlier work this paper cites.
A. Santos, P. A. Santos, and F. S. Melo, “Monte carlo tree search experiments in hearthstone,” in 2017 IEEE Conference on Computational Intelligence and Games (CIG) . IEEE, 2017, pp. 272–279
2017
Earlier work this paper cites.
A. Stiegler, K. P. Dahal, J. Maucher, and D. Livingstone, “Symbolic reasoning for hearthstone,” IEEE Transactions on Games , vol. 10, no. 2, pp. 113–127, 2017
2017
Earlier work this paper cites.
A. Janusz, T. Tajmajer, and M. Świechowski, “Helping ai to play hearthstone: Aaia’17 data mining challenge,” in 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) . IEEE, 2017, pp. 121–125
2017
Cited alongside, same era.
S. Zhang and M. Buro, “Improving hearthstone AI by learning high-level rollout policies and bucketing chance node events,” in 2017 IEEE Conference on Computational Intelligence and Games (CIG) . IEEE, 2017, pp. 309–316
2017
Cited alongside, same era.
I. Kachalsky, I. Zakirzyanov, and V. Ulyantsev, “Applying reinforcement learning and supervised learning techniques to play hearthstone,” in 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) . IEEE, 2017, pp. 1145–1148
2017
Cited alongside, same era.
M. Eger and P. Sauma Chacón, “Deck archetype prediction in hearthstone,” in International Conference on the Foundations of Digital Games , 2020, pp. 1–11
2020
Later among the works it cites.
M. C. Fontaine, J. Togelius, S. Nikolaidis, and A. K. Hoover, “Covariance matrix adaptation for the rapid illumination of behavior space,” Proceedings of the 2020 Genetic and Evolutionary Computation Conference , Jun 2020. [Online]. Available: http://dx.doi.org/10.1145/3377930.3390232
2020
Later among the works it cites.
C. Colas, V. Madhavan, J. Huizinga, and J. Clune, “Scaling map-elites to deep neuroevolution,” in Proceedings of the 2020 Genetic and Evolutionary Computation Conference , 2020, pp. 67–75
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
P. García-Sánchez, A. Tonda, A. M. Mora, G. Squillero, and J. J. Merelo, “Automated playtesting in collectible card games using evolutionary algorithms: A case study in hearthstone,” Knowledge-Based Systems , vol. 153, pp. 133–146, 2018
2018
Cited alongside, same era.
A. Bhatt, S. Lee, F. de Mesentier Silva, C. W. Watson, J. Togelius, and A. K. Hoover, “Exploring the hearthstone deck space,” in Proceedings of the 13th International Conference on the Foundations of Digital Games , 2018, pp. 1–10
2018
Cited alongside, same era.
A. Gaier, A. Asteroth, and J.-B. Mouret, “Data-efficient design exploration through surrogate-assisted illumination,” Evolutionary computation , vol. 26, no. 3, pp. 381–410, 2018
2018
Cited alongside, same era.
Z. Chen, C. Amato, T.-H. Nguyen, S. Cooper, Y. Sun, and M. S. El-Nasr, “Q-deckrec: A fast deck recommendation system for collectible card games,” 2018
2018
Cited alongside, same era.
A. Dockhorn, M. Frick, Ü. Akkaya, and R. Kruse, “Predicting opponent moves for improving hearthstone AI,” in International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems . Springer, 2018, pp. 621–632
2018
Cited alongside, same era.
M. Świechowski, T. Tajmajer, and A. Janusz, “Improving hearthstone AI by combining mcts and supervised learning algorithms,” in 2018 IEEE Conference on Computational Intelligence and Games (CIG) . IEEE, 2018, pp. 1–8
2018
Cited alongside, same era.
J. Jakubik, “A neural network approach to hearthstone win rate prediction,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS) . IEEE, 2018, pp. 185–188
2018
Cited alongside, same era.
J. Nordmoen, E. Samuelsen, K. O. Ellefsen, and K. Glette, “Dynamic mutation in map-elites for robotic repertoire generation,” in Artificial Life Conference Proceedings . MIT Press, 2018, pp. 598–605
2018
Cited alongside, same era.
2020
Later among the works it cites.
A. Hagg, D. Wilde, A. Asteroth, and T. Bäck, “Designing air flow with surrogate-assisted phenotypic niching,” in International Conference on Parallel Problem Solving from Nature . Springer, 2020, pp. 140–153
2020
Later among the works it cites.
A. Gaier, A. Asteroth, and J.-B. Mouret, “Discovering representations for black-box optimization,” Proceedings of the 2020 Genetic and Evolutionary Computation Conference , Jun 2020. [Online]. Available: http://dx.doi.org/10.1145/3377930.3390221
2020
Later among the works it cites.
L. Keller, D. Tanneberg, S. Stark, and J. Peters, “Model-based quality-diversity search for efficient robot learning,” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 9675–9680, 2020
2020
Later among the works it cites.
F. Veenstra, P. G. de Prado Salas, K. Stoy, J. Bongard, and S. Risi, “Death and progress: How evolvability is influenced by intrinsic mortality,” Artificial life , vol. 26, no. 1, pp. 90–111, 2020
2020
Later among the works it cites.
D. Karavolos, A. Liapis, and G. N. Yannakakis, “A multifaceted surrogate model for search-based procedural content generation,” IEEE Transactions on Games , vol. 13, pp. 11–22, 2021
2021
Closest in time.
B. Trabucco, A. Kumar, X. Geng, and S. Levine, “Conservative objective models for effective offline model-based optimization,” in International Conference on Machine Learning . PMLR, 2021, pp. 10 358–10 368
2021
Closest in time.
E. Bursztein, “Predicting hearthstone game outcome with machine learning,” https://elie.net/blog/hearthstone/how-to-find-automatically-hearthstone-undervalued-cards/ , https://elie.net/blog/hearthstone/predicting-hearthstone-game-outcome-with-machine-learning/ , published October 2016; Accessed July 2021
2021
Closest in time.
K. Chatzilygeroudis, A. Cully, V. Vassiliades, and J.-B. Mouret, “Quality-diversity optimization: a novel branch of stochastic optimization,” in Black Box Optimization, Machine Learning, and No-Free Lunch Theorems . Springer, 2021, pp. 109–135
2021
Closest in time.
M. C. Fontaine and S. Nikolaidis, “Differentiable quality diversity,” 2021
2021
Closest in time.
M. Fontaine and S. Nikolaidis, “A quality diversity approach to automatically generating human-robot interaction scenarios in shared autonomy,” Robotics: Science and Systems , 2021
2021
Closest in time.
M. C. Fontaine, Y.-C. Hsu, Y. Zhang, B. Tjanaka, and S. Nikolaidis, “On the importance of environments in human-robot coordination,” Robotics: Science and Systems , 2021
2021
Closest in time.
N. Rakicevic, A. Cully, and P. Kormushev, “Policy manifold search: Exploring the manifold hypothesis for diversity-based neuroevolution,” in Proceedings of the Genetic and Evolutionary Computation Conference , ser. GECCO ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 901–909
2021
Closest in time.
2021
Closest in time.
r. darkfriend77, “Sabberstone,” https://github.com/icaros-usc/SabberStone , 2021
2021
Closest in time.
Stonekeep, “Classic miracle rogue,” Hearthstone Top Decks, 2021. [Online]. Available: https://www.hearthstonetopdecks.com/decks/classic-miracle-rogue-2/
2021
Closest in time.