Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 37 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 10 tok/s Pro
GPT-5 High 15 tok/s Pro
GPT-4o 84 tok/s Pro
Kimi K2 198 tok/s Pro
GPT OSS 120B 448 tok/s Pro
Claude Sonnet 4 31 tok/s Pro
2000 character limit reached

Introducing Tales of Tribute AI Competition (2305.08234v4)

Published 14 May 2023 in cs.AI

Abstract: This paper presents a new AI challenge, the Tales of Tribute AI Competition (TOTAIC), based on a two-player deck-building card game released with the High Isle chapter of The Elder Scrolls Online. Currently, there is no other AI competition covering Collectible Card Games (CCG) genre, and there has never been one that targets a deck-building game. Thus, apart from usual CCG-related obstacles to overcome, like randomness, hidden information, and large branching factor, the successful approach additionally requires long-term planning and versatility. The game can be tackled with multiple approaches, including classic adversarial search, single-player planning, and Neural Networks-based algorithms. This paper introduces the competition framework, describes the rules of the game, and presents the results of a tournament between sample AI agents.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. J. Togelius, “AI Researchers, Video Games Are Your Friends!” in Computational Intelligence, 2017, pp. 3–18.
  2. M. Campbell, A. J. Hoane, and F. Hsu, “Deep Blue,” Artificial intelligence, vol. 134, no. 1, pp. 57–83, 2002.
  3. G. Tesauro, “TD-Gammon, a self-teaching backgammon program, achieves master-level play,” Neural computation, vol. 6, no. 2, pp. 215–219, 1994.
  4. H. Finnsson and Y. Björnsson, “Simulation-based approach to general game playing.” in AAAI, vol. 8, 2008, pp. 259–264.
  5. V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, 2015.
  6. D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” nature, vol. 529, no. 7587, pp. 484–489, 2016.
  7. A. K. Hoover, J. Togelius, S. Lee, and F. de Mesentier Silva, “The Many AI Challenges of Hearthstone,” KI-Künstliche Intelligenz, vol. 34, pp. 33–43, 2020.
  8. A. Dockhorn and S. Mostaghim, “Introducing the Hearthstone-AI Competition,” ArXiv, vol. abs/1906.04238, 2019.
  9. J. Kowalski and R. Miernik, “Summarizing Strategy Card Game AI Competition,” in COG, 2023, pp. 1–8.
  10. R. van der Heijden, “An Analysis of Dominion,” Bachelor’s Thesis, Leiden University, 2014.
  11. C. B. Browne et al., “A Survey of Monte Carlo Tree Search Methods,” IEEE TCIAIG, vol. 4, no. 1, pp. 1–43, 2012.
  12. P. García-Sánchez, A. Tonda, A. J. Fernández-Leiva, and C. Cotta, “Optimizing hearthstone agents using an evolutionary algorithm,” Knowledge-Based Systems, vol. 188, p. 105032, 2020.
  13. R. Montoliu et al., “Efficient Heuristic Policy Optimisation for a Challenging Strategic Card Game,” in Applications of Evolutionary Computation, ser. LNTCS, vol. 12104, 2020, pp. 403–418.
  14. R. Miernik and J. Kowalski, “Evolving Evaluation Functions for Collectible Card Game AI,” in ICAART.   SciTePress, 2022, pp. 253–260.
  15. J. S. B. Choe and J.-K. Kim, “Enhancing Monte Carlo Tree Search for Playing Hearthstone,” in COG, 2019, pp. 1–7.
  16. S. Zhang and M. Buro, “Improving Hearthstone AI by learning high-level rollout policies and bucketing chance node events,” in CIG, 2017, pp. 309–316.
  17. M. Świechowski, T. Tajmajer, and A. Janusz, “Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms,” in CIG, 2018, pp. 1–8.
  18. W. Xi et al., “Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play,” ArXiv, vol. abs/2303.04096, 2023.
  19. C. Xiao, Y. Zhang, X. Huang, Q. Huang, J. Chen, and P. Sun, “Mastering Strategy Card Game (Hearthstone) with Improved Techniques,” ArXiv, vol. abs/2303.05197, 2023.
  20. J. V. Jansen and R. Tollisen, “An ai for dominion based on monte-carlo methods,” Master’s thesis, University of Agder, 2014.
  21. J. Gerigk and S. Engels, “Playing various strategies in dominion with deep reinforcement learning,” AIIDE, vol. 19, no. 1, pp. 224–232, 2023.
  22. P. García-Sánchez, A. Tonda, G. Squillero, A. Mora, and J. J. Merelo, “Evolutionary deckbuilding in Hearthstone,” in CIG, 2016, pp. 1–8.
  23. J. Kowalski and R. Miernik, “Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes,” in CEC, 2020, pp. 1–8.
  24. M. C. Fontaine et al., “Mapping Hearthstone Deck Spaces Through MAP-elites with Sliding Boundaries,” in GECCO, 2019, pp. 161–169.
  25. F. de Mesentier Silva et al., “Evolving the Hearthstone meta,” in COG, 2019, pp. 1–8.
  26. T. Mahlmann, J. Togelius, and G. N. Yannakakis, “Evolving card sets towards balancing dominion,” in CEC, 2012, pp. 1–8.
  27. D. Budzki, D. Kowalik, and K. Polak, “Implementing Tales of Tribute as a Programming Game,” Engineer’s Thesis, University of Wrocław, 2023.
  28. D. Kowalczyk, J. Kowalski, H. Obrzut, M. Maras, S. Kosakowski, and R. Miernik, “Developing a Successful Bomberman Agent,” in ICAART, vol. 2, 2022, pp. 335–344.
  29. A. Ciężkowski and A. Krzyżyński, “Developing Card Playing Agent for Tales of Tribute AI Competition,” Engineer’s Thesis, University of Wrocław, 2023.
Citations (3)

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com
Youtube Logo Streamline Icon: https://streamlinehq.com