Abou-Zleikha, Mohamed

Shaker, Noor

We present a demonstration of PaTux, an authoring tool for designing levels in SuperTux game through combining patterns. PaTux allows game designers to specify the design of their levels using patterns extracted from training level samples. The Non-negative Matrix Factorisation (NMF) method is utilised to approximate pattern and weight matrices from the training data. The patterns are visualised for designers to choose from and the changes made on the level structure are visualised in realtime. The designer can also specify the weight of each pattern permitting exploration of a wider variety. The data used to train the model can also be specified by the designer resulting in learning a new set of patterns. The system also suggests variations for a given design. When the designer is satisfied with the design, the system allows loading the resultant level in the game to be played

    Aalborg UniversitetPaTuxAbou-Zleikha, Mohamed; Shaker, NoorPublished in:PROCEEDINGS OF THE TENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERACTIVEDIGITAL ENTERTAINMENT (AIIDE-14)Publication date:2014Document VersionEarly version, also known as pre-printLink to publication from Aalborg UniversityCitation for published version (APA):Abou-Zleikha, M., & Shaker, N. (2014). PaTux: An Authoring Tool for Level Design through PatternCustomisation Using Non-Negative Matrix Factorization. In PROCEEDINGS OF THE TENTH AAAICONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERACTIVE DIGITAL ENTERTAINMENT (AIIDE-14)(pp. 103-104). AAAI Press.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            ? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            ? You may not further distribute the material or use it for any profit-making activity or commercial gain            ? You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: April 25, 2017PaTux: An Authoring Tool for Level Design through Pattern Customisation UsingNon-Negative Matrix FactorizationMohamed Abou-Zleikha1 and Noor Shaker21Department of Electronic Systems, Aalborg University, Aalborg, Denmark2Center for Computer Games and Interaction Design at the IT University of Copenhagen, Copenhagen, Denmarknosh@itu.dk, moa@es.aau.dkAbstractWe present a demonstration of PaTux, an authoring tool fordesigning levels in SuperTux game through combining pat-terns. PaTux allows game designers to specify the design oftheir levels using patterns extracted from training level sam-ples. The Non-negative Matrix Factorisation (NMF) methodis utilised to approximate pattern and weight matrices fromthe training data. The patterns are visualised for designersto choose from and the changes made on the level structureare visualised in realtime. The designer can also specify theweight of each pattern permitting exploration of a wider vari-ety. The data used to train the model can also be specified bythe designer resulting in learning a new set of patterns. Thesystem also suggests variations for a given design. When thedesigner is satisfied with the design, the system allows load-ing the resultant level in the game to be played.1 IntroductionLevel design is an essential part of the game design process.Designers usually put much of their time and effort in cre-ating an interesting, playable level. This process is typicallyiterative between generating and testing until the designer issatisfied with the result. The last few years have witnessedthe flourish of independent video games. This has increasedthe demand for low-budget tools that are affordable and easyto use. Partly as a response to this need, there has recentlybeen increasing interest in academia given to the creation ofauthoring and mixed-initiative tools. Some notable works in-clude SkectchaWorld a tool for modelling 3D world (Smeliket al. 2010), Tanagra a mixed-initiative design tool for 2Dplatformer (Smith, Whitehead, and Mateas 2010), SentientSketchbook a tool that aids the design of game maps (Liapis,Yannakakis, and Togelius 2013), and Ropossum a tool thatpermits automatic generation and testing of physics-basedpuzzle games (Shaker, Shaker, and Togelius 2013).In this paper, we introduce PaTux, an authoring tool forlevel generation through combining patterns. The patternsused are extracted using the NMF method that is applied ona large dataset of training level samples. The tool permits se-lecting patterns for different game components, customisingtheir weights to provide variations, realtime visualisation ofedits and it permits playing the created design. The designerCopyright c© 2014, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.can also retrain the model on his own dataset which resultsin the extraction of a new set of patterns that represents thestyle of the generator used to create the truing set.2 Testbed Game: SuperTuxSuperTux (SuperTux Development 2003) is a free opensource 2D platform game inspired by Super Mario Bros. Weuse the source code available for the Platformer AI Compe-tition (Shaker, Togelius, and Yannakakis 2013) to generatethe training data and to implement the tool.3 The NMF ModelNon-Negative Matrix Factorization (NMF) is a technique forrepresenting non-negative data as a linear parts-based com-binations (Lee and Seung 1999). The non-negativity prop-erty makes NMF results easy to inspect since the represen-tation is purely additive.Given a non-negative data matrix V, NMF finds an ap-proximate factorization V ≈ WH where W and H arenon-negative factors. The objective function of NMF isto minimize the Euclidean distance between each columnof the matrix V and its approximation V′ = WH. Thecolumns of the matrix W can be seen as parts (i.e. build-ing blocks) of the data while the coefficient vectors describehow strongly each part is present.Utilising NMF for level generation corresponds to the ex-traction of the patterns matrix W from a set of training lev-els V . In order to construct the V matrix, the structure of thelevels is converted into one dimensional vectors of numberscorresponding to platform structure Vp, hills Vh, gaps Vg ,items Vt and enemies Ve. The final model consists of five in-dependent NMF models constructed for each level compo-nent. Fig. 1 illustrates the framework followed. More detailscan be found in (Shaker and Abou-Zleikha 2014).4 The NMF GeneratorOnce the model is built and the Ws and Hs matrices areestimated, the model can be used to generate a new level. Acoefficient vector h′ representing the weights applied on theestimated pattern matrices Ws is set as input to the model(see Fig. 1). Reconstructing a single levels corresponds toestimating each of its basic components independently. Con-sequently, the results are < V′p,V′h,V′g,V′t,V′e > thatProceedings of the Tenth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2014)103Figure 1: The framework implemented to estimate the model andto use it to generate new content. The upper part shows the stepsfollowed to construct the five NMFs. Levels are first converted intosequences of numbers and arranged in the V matrices that are usedas input to estimate the W and H matrices for each level compo-nent. In the content generation phase, and to create a new level, acoefficient vector is used as input. The result is a vector represent-ing the content of the new level.Figure 2: A snapshot from the interface provided with the tool.The upper part presents the initial level design which is modifiedin realtime to provide visual feedback about designer’s choices.Patterns of different components are presented in the lower frames.The designer can activate/deactivate each pattern and change itsweight and edits will be directly reflected in the upper frame.represents a newly generated level. These vectors are thenparsed and composed to create a level in its 2D map formatthat can be loaded and played.5 DemonstrationFig. 2 presents the user interface of the authoring tool. Fivetabs are used to show patterns of the game components.Designers can activate a number of patterns, change theirweights and reflections of the edits are directly visualised.Designers can also navigate between the taps to enrich theirdesign by adding items from different components. Fig. 3presents a snapshot for the same level presented in Fig. 2after performing a number of edits and adding different arti-facts including hills, enemies and coins.After finalising a design, the tool provides the option ofvisualising similar variations such as the ones presentedin Fig. 4. These variations are generated by assigning lowweights for a randomly chosen set of unselected patterns. Atany point during the level design process, the designer canplay the level to get more thorough insights on the experi-ence it elicits.Figure 3: The final design of a level after performing several editsand adding patterns from different components.Figure 4: Ten variations for the level presented in Fig. 3.The tool also allows retraining the models on another setof levels leading to the extraction of new patterns. The train-ing set can be chosen by the designer and can be anotherset of automatically generated levels, or a number of hand-crafted levels. In both cases, representative patterns of thestyle of the training generator will be extracted.6 AcknowledgementThe research is supported in part by the Danish ResearchAgency, Ministry of Science, Technology and Innovation;project “PlayGALe” (1337-00172) and supported in part bythe Danish Council for Strategic Research of the DanishAgency for Science Technology and Innovation under theCoSound project, case number 11-115328. This publicationonly reflects the authors views.ReferencesLee, D. D., and Seung, H. S. 1999. Learning the parts of objectsby non-negative matrix factorization. Nature 401(6755):788–791.Liapis, A.; Yannakakis, G.; and Togelius, J. 2013. Sentient sketch-book: Computer-aided game level authoring. In Proceedings ofACM Conference on Foundations of Digital Games.Shaker, N., and Abou-Zleikha, M. 2014. Alone we can do solittle, together we can do so much: A combinatorial approach forgenerating game content. In AIIDE.Shaker, M.; Shaker, N.; and Togelius, J. 2013. Ropossum: Anauthoring tool for designing, optimizing and solving cut the ropelevels. In Proceedings of the AAAI Conference on Artificial Intelli-gence and Interactive Digital Entertainment (AIIDE). AAAI Press.Shaker, N.; Togelius, J.; and Yannakakis, G. 2013. Platformer AICompetition.Smelik, R.; Tutenel, T.; de Kraker, K.; and Bidarra, R. 2010. Inte-grating procedural generation and manual editing of virtual worlds.In Proceedings of the 2010 Workshop on Procedural Content Gen-eration in Games, 2. ACM.Smith, G.; Whitehead, J.; and Mateas, M. 2010. Tanagra: A mixed-initiative level design tool. In Proceedings of the Fifth InternationalConference on the Foundations of Digital Games, 209–216. ACM.SuperTux Development. 2003. SuperTux.104

PaTux:An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization

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PaTux:An Authoring Tool for Level Design through Pattern Customisation Using Non-Negative Matrix Factorization

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