Hearthstone deck-construction with a utility system

Trading Card Games are turn-based games involving strategic planning, synergies and rather complex gameplay. An interesting aspect of this game domain is the strong influence of their metagame: in this particular case deck-construction. Before a game starts, players select which cards from a vast card pool they want to take into the current game session, defining their available options and a great deal of their strategy. We introduce an approach to do automatic deck construction for the digital Trading Card Game Hearthstone, based on a utility system utilizing several metrics to cover gameplay concepts such as cost effectiveness, the mana curve, synergies towards other cards, strategic parameters about a deck as well as data on how popular a card is within the community. The presented approach aims to provide useful information about a deck for a player-level AI playing the actual game session at runtime. Herein, the key use case is to store information on why cards were included and how they should be used in the context of the respective deck. Besides creating new decks from scratch, the algorithm is also capable of filling holes in existing deck skeletons, fitting an interesting use case for Human Hearthstone players: adapting a deck to their specific pool of available cards. After introducing the algorithms and describing the different utility sources used, we evaluate how the algorithm performs in a series of experiments filling holes in existing decks of the Hearthstone eSports scene.

[1]  Peter I. Cowling,et al.  Monte Carlo search applied to card selection in Magic: The Gathering , 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

[2]  David Kirsh,et al.  A Corpus Analysis of Strategy Video Game Play in Starcraft: Brood War , 2011, CogSci.

[3]  Elie Bursztein,et al.  I am a legend: hacking hearthstone with machine learning , 2014 .

[4]  Miguel Nussbaum,et al.  A Model to Support the Design of Multiplayer Games , 2000, Presence: Teleoperators & Virtual Environments.

[5]  Emilio Arnieri,et al.  Support Vector Regression Machines to Evaluate Resonant Frequency of Elliptic Substrate Integrate Waveguide Resonators , 2008 .

[6]  Georgios N. Yannakakis.  Game AI revisited , 2012, CF '12.

[7]  John Krumm,et al.  Object recognition with color cooccurrence histograms , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[8]  Michael Buro,et al.  Call for AI Research in RTS Games , 2004 .

[9]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[10]  Santiago Ontañón,et al.  A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[11]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[12]  Michael Buro,et al.  RTS Games as Test-Bed for Real-Time AI Research , 2003 .

[13]  Juho Hamari,et al.  Free-to-Play Games: Professionals’ Perspectives , 2014 .

[14]  Esko Vankka,et al.  Free-to-Play Games: Professionals' Perceptions , 2014 .