Oh Gosh!! Why is this game so hard? Identifying cycle patterns in 2D platform games using provenance data

Abstract There are many elements that make a game interesting for the player. The game play is a key element. It is the way in which players interact with a game. In general, this interaction starts easier and becomes more challenge along the game session. Commonly game developers try to make balanced games, but due to the big variety of age, genre and expertise among players, this is not a trivial task and sometimes the game may become too difficult for some users, thus becoming boring. In this article we focus on identifying one aspect in special: the cycles in a game session, which represent a sequence of actions that are played repeatedly by the player in certain parts of the game. These cycles tend to make the game tedious. This way, it is a top priority for game designers to identify cycles. In this article we propose an approach identifies cycles in games using sequential pattern mining algorithms over provenance data collected from the game session. Using the proposed approach, we are able to identify cycles in the game session and we evaluated the feasibility of our proposal with the 2D game “Super Mario World”, a well-known commercial title.

[1]  Sanjeev Khanna,et al.  Differencing Provenance in Scientific Workflows , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[2]  Wolfgang F. Engel GPU Pro 5 : Advanced Rendering Techniques , 2014 .

[3]  Marta Mattoso,et al.  Towards a Taxonomy of Provenance in Scientific Workflow Management Systems , 2009, 2009 Congress on Services - I.

[4]  Paul T. Groth,et al.  The provenance of electronic data , 2008, CACM.

[5]  Juliana Freire,et al.  Provenance and scientific workflows: challenges and opportunities , 2008, SIGMOD Conference.

[6]  Marta Mattoso,et al.  A Performance Evaluation of X-Ray Crystallography Scientific Workflow Using SciCumulus , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[7]  Len Noriega,et al.  Games analysis: how to stop history repeating itself , 2006 .

[8]  Marta Mattoso,et al.  Towards supporting the life cycle of large scale scientific experiments , 2010, Int. J. Bus. Process. Integr. Manag..

[9]  Johannes Gehrke,et al.  Better scripts, better games , 2009, CACM.

[10]  Esteban Walter Gonzalez Clua,et al.  Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data , 2016, IPAW.

[11]  Sattar Hashemi,et al.  Towards a variable size sliding window model for frequent itemset mining over data streams , 2012, Comput. Ind. Eng..

[12]  Kamran Sedig,et al.  Interaction design and cognitive gameplay: role of activation time , 2014, CHI PLAY.

[13]  Esteban Walter Gonzalez Clua,et al.  Game Flux Analysis with Provenance , 2013, Advances in Computer Entertainment.

[14]  Leonardo Gresta Paulino Murta,et al.  What Should I Code Now? , 2014, J. Univers. Comput. Sci..

[15]  Chedy Raïssi,et al.  Towards bounding sequential patterns , 2011, KDD.

[16]  John F. Roddick,et al.  Sequential pattern mining -- approaches and algorithms , 2013, CSUR.

[17]  Marta Mattoso,et al.  SciCumulus: A Lightweight Cloud Middleware to Explore Many Task Computing Paradigm in Scientific Workflows , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[18]  Esteban Walter Gonzalez Clua,et al.  A Non-intrusive Approach for 2D Platform Game Design Analysis Based on Provenance Data Extracted from Game Streaming , 2014, 2014 Brazilian Symposium on Computer Games and Digital Entertainment.

[19]  Esteban Clua,et al.  A Game Design Analytic System Based on Data Provenance , 2013, ICEC 2013.

[20]  Cláudio T. Silva,et al.  Provenance for Computational Tasks: A Survey , 2008, Computing in Science & Engineering.

[21]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[22]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[23]  Luc Moreau,et al.  The Open Provenance Model: An Overview , 2008, IPAW.

[24]  Marta Mattoso,et al.  Towards a Taxonomy for Cloud Computing from an e-Science Perspective , 2010, Cloud Computing.

[25]  Wen-mei W. Hwu,et al.  Optimization principles and application performance evaluation of a multithreaded GPU using CUDA , 2008, PPoPP.

[26]  Philippe Fournier-Viger,et al.  Un modèle hybride pour le support à l'apprentissage dans les domaines procéduraux et mal définis , 2010 .

[27]  John Seng,et al.  Sidewalk following using color histograms , 2008 .

[28]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[29]  Ferenc Bodon,et al.  A trie-based APRIORI implementation for mining frequent item sequences , 2005 .

[30]  Arup Kumar Pal,et al.  A Content Based Image Retrieval using Color and Texture Features , 2016 .

[31]  Marta Mattoso,et al.  Capturing and querying workflow runtime provenance with PROV: a practical approach , 2013, EDBT '13.

[32]  Suh-Yin Lee,et al.  Mining frequent itemsets over data streams using efficient window sliding techniques , 2009, Expert Syst. Appl..