HoningStone: Building Creative Combos With Honing Theory for a Digital Card Game

In recent years, online digital games have left behind the status of entertainment sources to become also professional electronic sports. Worldwide championships offer prizes up to millions of dollars for the best competitors and/or teams among different game categories such as digital collectible card games (DCCG), multiplayer online battle arena, etc. Hearthstone, by Blizzard Entertainment, is a DCCG that has an increasing number of players up to the millions. In this game, individual players compete in one-versus-one matches in alternating turns, until a player is defeated. The greatest challenge in this game is to build a deck of cards and a strategy to combine these cards in order to be competitive against other players without a priori knowledge about their decks and strategies. This is a daunting task that requires deep knowledge of each existing card and great amount of creativity to surprise adversaries in this very adaptive environment. This paper presents a computational system, called HoningStone, that automatically generates creative card combos based on the honing theory of creativity. Our experimental results show that HoningStone can generate combos that are more creative than a greedy randomized algorithm driven by a creativity metric.

[1]  Kazjon Grace,et al.  What to expect when you're expecting: The role of unexpectedness in computationally evaluating creativity , 2014, ICCC.

[2]  Kush R. Varshney,et al.  Cognition as a part of computational creativity , 2013, 2013 IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing.

[3]  Julian Togelius,et al.  Towards the automatic generation of card games through grammar-guided genetic programming , 2013, FDG.

[4]  Pierre Baldi,et al.  Of bits and wows: A Bayesian theory of surprise with applications to attention , 2010, Neural Networks.

[5]  E. Grigorenko,et al.  The Genetics of Creativity: The Generative and Receptive Sides of the Creativity Equation , 2013 .

[6]  Mathias Benedek,et al.  Enhancement of Ideational Fluency by Means of Computer-Based Training , 2006 .

[7]  Simon Colton,et al.  Ludus Ex Machina: Building A 3D Game Designer That Competes Alongside Humans , 2014, ICCC.

[8]  Terri Gullickson.  The Creative Mind: Myths and Mechanisms. , 1995 .

[9]  Inderjeet Mani,et al.  Computational Modeling of Narrative , 2013, Computational Modeling of Narrative.

[10]  L. Gabora.  Revenge of the “Neurds”: Characterizing Creative Thought in Terms of the Structure and Dynamics of Memory , 2010, 1308.5037.

[11]  Simon Colton,et al.  Mining Rules from Player Experience and Activity Data , 2012, AIIDE.

[12]  Tony Veale,et al.  Twitter: The Best of Bot Worlds for Automated Wit , 2015, HCI.

[13]  Ashok K. Goel,et al.  Interactive Meta-Reasoning: Towards a CAD-Like Environment for Designing Game-Playing Agents , 2015 .

[14]  Julian Togelius,et al.  Computational Game Creativity , 2014, ICCC.

[15]  Simon Colton,et al.  Automating Game Design In Three Dimensions , 2014 .

[16]  Olivier Teytaud,et al.  Upper Confidence Trees with Short Term Partial Information , 2011, EvoApplications.

[17]  Simon Colton,et al.  Computational Creativity: The Final Frontier? , 2012, ECAI.

[18]  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.

[19]  Simon Dixon,et al.  A Four Strategy Model of Creative Parameter Space Interaction , 2014, ICCC.

[20]  Peter I. Cowling,et al.  Heuristic move pruning in Monte Carlo Tree Search for the strategic card game Lords of War , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[21]  Julian Togelius,et al.  Evolving card sets towards balancing dominion , 2012, 2012 IEEE Congress on Evolutionary Computation.

[22]  Simon Colton,et al.  Computational creativity in a closed game system , 2012, 2012 IEEE Conference on Computational Intelligence and Games (CIG).