Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge

This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for the assessment of player's winning chances can be utilized in a construction of an intelligent agent for playing Hearthstone. Finally, we show a few selected machine learning approaches for modeling state and action values in Hearthstone. We provide evaluation for a few promising solutions that may be used to create more advanced types of agents, especially in conjunction with Monte Carlo Tree Search algorithms.

[1]  Dominik Slezak,et al.  Feedforward neural networks for compound signals , 2011, Theor. Comput. Sci..

[2]  Olivier Teytaud,et al.  Modification of UCT with Patterns in Monte-Carlo Go , 2006 .

[3]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[4]  Dominik Slezak,et al.  Knowledge Pit - A Data Challenge Platform , 2015, CS&P.

[5]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[6]  Aaron Qiu,et al.  Learning Artificial Intelligence in Large-Scale Video Games — A First Case Study with Hearthstone : Heroes of WarCraft , 2015 .

[7]  Dominik Slezak,et al.  Interactive Gene Clustering—A Case Study of Breast Cancer Microarray Data , 2006, Inf. Syst. Frontiers.

[8]  Juan Julián Merelo Guervós,et al.  Evolutionary deckbuilding in hearthstone , 2016, 2016 IEEE Conference on Computational Intelligence and Games (CIG).

[9]  H. Jaap van den Herik,et al.  Progressive Strategies for Monte-Carlo Tree Search , 2008 .

[10]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Kyung-Joong Kim,et al.  Recent Advances in General Game Playing , 2015, TheScientificWorldJournal.

[12]  Jacek Mandziuk,et al.  An Automatically Generated Evaluation Function in General Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[13]  Saharon Rosset,et al.  Leakage in data mining: formulation, detection, and avoidance , 2011, TKDD.

[14]  Andrzej Janusz.  Combining multiple predictive models using genetic algorithms , 2012, Intell. Data Anal..

[15]  Maciej Swiechowski,et al.  Self-Adaptation of Playing Strategies in General Game Playing , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[16]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[17]  Donald C. Wunsch,et al.  Computer Go: A Grand Challenge to AI , 2007, Challenges for Computational Intelligence.

[18]  Simon M. Lucas,et al.  A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.

[19]  Simon M. Lucas,et al.  Knowledge-based fast evolutionary MCTS for general video game playing , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[20]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[21]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.