This paper outlines a system for generating adventure games based on open data, and describes a sketch of the system implementation at its current state. The adventure game genre has been popular for a long time and differs signi cantly in design priorities from game genres which are commonly addressed in PCG research. In order to create believable and engaging content, we use data from DBpedia to generate the game's non-playable characters locations and plot, and OpenStreetMaps to create the game's levels.peer-reviewe

Barros, Gabriella A. B.

FDG workshop on Procedural Content Generation in Games

Liapis, Antonios

Togelius, Julian

OAR@UM

Data AdventuresGabriella A. B. BarrosCenter for ComputerGames ResearchIT University of CopenhagenCopenhagen, Denmarkgbar@itu.dkAntonios LiapisInstitute of Digital GamesUniversity of MaltaMsida, Maltaantonios.liapis@um.edu.mtJulian TogeliusDepartment of ComputerScience and EngineeringNew York UniversityNew York, USAjulian@togelius.comABSTRACTThis paper outlines a system for generating adventure gamesbased on open data, and describes a sketch of the system im-plementation at its current state. The adventure game genrehas been popular for a long time and differs significantly indesign priorities from game genres which are commonly ad-dressed in PCG research. In order to create believable andengaging content, we use data from DBpedia to generatethe game’s non-playable characters locations and plot, andOpenStreetMaps to create the game’s levels.1. INTRODUCTIONMuch work in procedural content generation (PCG) is cur-rently focused on generating content such as levels, mapsand items for mechanics-heavy games, such as action gamesand puzzle games. Similarly, attempts to generate completegames have focused on simple arcade games or board games,with nothing in the way of story [12].In a sense, most of PCG and game generation focuseson “silent” content, reflecting a formalist mechanics-first ap-proach to games. At the same time, research in interactivenarrative focuses on generating stories first, where gameplayis contingent on the stories. In this paper, we seek inspi-ration in an existing and successful narrative-heavy gamegenre, with strong genre conventions, and propose generat-ing complete games in this genre following the structuralconstraints given by these genre conventions.We look at the genre of adventure games, as exempli-fied by games such as Maniac Mansion (Lucasfilm Games1987), Day of the Tentacle (Lucas Arts 1993) and in partic-ular Where in the world is Carmen Sandiego? (BrøderbundSoftware 1985), as a challenging novel domain for game gen-eration methods. This poses new challenges for content gen-eration and game generation. In particular, the generatedgame needs to be meaningful in terms of its textual descrip-tion as well as in terms of game mechanics. To take CarmenSandiego as an example, the game content to a large extentconsists of descriptions of existing cities and places withincities. Simply generating random names and descriptions islikely to lead to an underwhelming experience.In order to generate such content, we turn to the practiceof data games, i.e. generating game content based on opendata [6]. The basic idea is that the real world already con-tains almost everything we need to know in order to (auto-matically) create adventure games; design is mostly a matterof selecting and ordering data so as to fit with genre conven-tions, and perhaps add a few extraneous elements. Hence, inorder to generate an adventure game, we make use of opendata to provide both the structure and the associations be-tween game elements. Networks of information, as foundin Wikipedia, can provide with the necessary links betweendissimilar objects (people, locations, objects, topics). Suchlinks can be used to create a rudimentary plot, which theplayer needs to discover. Adding more depth to these dis-covered links, images and maps from online databases canbe used to enhance both gameplay and aesthetic appeal ofthe game. The first steps to implementing such a game aredescribed in this paper.1.1 Game generation and content generationProcedural generation of game content is a common fea-ture in several game genres and an active research field [10].Content generation can be more or less ambitious in scope;the proposed project seeks to generate essentially the wholeadventure game, while simply keeping some core mechanicsfixed. Many different efforts have been made to generatecomplete games, using methods that are solver-based, con-structive or search-based [12].Search-based game generation uses optimization algo-rithms (e.g. evolutionary computation) to search a space forfeasible games. Ludi generates board games through evolu-tionary computation, evaluating the games via a weightedsum of many heuristics [2]. A number of attempts have beenmade to generate similar simple mechanics-focused games [13,4, 9]. It has even been argued that this type of games— single-player, short mechanics-heavy games with easilyquantifiable properties — are ideal for generation as theycan be evaluated easily through simulated playthroughs [12].Instead, the proposed project aims to generate gameswhich cannot easily be evaluated based on whether an al-gorithm playing them wins or loses: losing the game is notpossible or not of much consequence. Instead, the game isfocused on progression through exploration. We assume avery limited basic vocabulary of mechanics, with most of theoptions for action being implicitly defined by the content.This opens up a set of new challenges for game and contentgeneration, most importantly what to base the content onso that it becomes meaningful and interesting to play with.1.2 Data gamesThe term “data games” describes games which use real-world open data to generate in-game content. In such games,data can be explored during gameplay, which allows theemergence of several ways to learn and visualize informa-tion [6, ?]. In most cases, data from the real world must betransformed in order to be useful as game content. Trans-formation typically involves both structural transformation,where data format is changed to work with the game, anddata selection, where parts or aspects of a dataset that isuseful for generating game content are selected.Examples of data games include Bar Chart Ball [11], wherethe player moves a ball atop a bar chart by choosing differ-ent demographic indicators which change the appearance ofthe chart. Other projects include Open Data Monopoly,which uses real-world demographic information to createa Monopoly (Parker Brothers 1935) board game [5], andOpen Trumps, which evolves sets of cards for Top Trumps(Dubreq 1977) based on countries’ data [3]. Similarly, datafrom OpenStreetMaps and resource maps have been used tocreate balanced Civilization (MicroProse 1991) maps [1].1.3 Adventure gamesAdventure games were extremely popular during the 1990s,with classic games such as The Secret of Monkey Island (Lu-casArts 1990) or Day of the Tentacle. Such games werehailed for their witty humor, their amusing dialogue anddifficult puzzles. Despite a decrease in commercial interestduring the 2000s, adventure games have resurfaced both ascommercially viable products and as popular entertainmentoutlets. Contemporary adventure games such as Tales ofMonkey Island (Telltale Games 2009) or Broken Age (Dou-ble Fine 2014) use episodic releases of the adventure’s story,thus retaining player interest for longer periods of time.While modern adventure games may be visually very dif-ferent than their counterparts in the 1990s, certain commonpatterns of play and design connect all adventure games to-gether. Adventure games often revolve around solving amystery or overcoming a challenge through cunning and un-expected associations between pieces of information or un-likely objects. The core challenge is the discovery of suchassociations, which may require that players perform spe-cific sequences of actions, combine objects in their inventoryto create new objects, or pay close attention to the hints pro-vided in dialogue with non-player characters (NPCs). Chal-lenges in most adventure games are local: a player mustsolve a puzzle in order for the story to move forward andthe next puzzle be presented. Some adventure games caninclude losing conditions, where a player needs to load orrestart from a previous game state, although more oftenplayers can always revisit all previous states of the gameand search for missing clues. Due to the carefully writtenend-game and plot progression, most adventure games areplayed once and have little replay value.In this project, we are particularly inspired by Where inthe world is Carmen Sandiego?, a series of popular educa-tional adventure games by Brøderbund Software. The firstgame in the series was released in 1985. In this game, theplayer has to find the eponymous Carmen Sandiego by trav-eling around the world and collecting clues as to Sandiego’sappearance and whereabouts. The destinations visited arereal institutions and monuments in real cities, though (most)NPCs in the game are fictional. The use of real world placesand phenomena suggests that the basic game structure couldform the basis for a type of data game.2. PROPOSED GAME DESIGNTraditionally, creating an adventure game is a laboriousprocess where clever dialogue and interesting storylines arecarefully authored by one or more writers. Adventures areoften driven by their story; therefore, plot, NPCs and dia-logues are carefully tuned to provide a rich user experience.In contrast, using open data to drive the system of an ad-venture game, we come across the challenge of designing agame that needs to interpret and understand raw input, andcan work in less controlled (or controllable) instances thantraditional adventure games.With this in mind, our goal is to create an adventure gamefocused mainly on discovery of visual and verbal clues andpieces of NPC dialogue. Fundamentally, the player’s goalis to find the location of a certain person, starting fromthe location of another person or clue. To do so, playerscan travel between different countries and cities, and explorebuildings in each city. Entering buildings within a city, theplayer can interact with NPCs and objects found there inorder to get clues which can unlock new locations, NPCsor dialogue options. Some objects or NPCs might give falseclues, and NPCs can give no clues at all. A clue can either bea line of dialogue or an object with some piece of information(e.g. a letter portraying a NPC that the player needs to talkto).Similar to many adventure games, it is not possible tolose the game, and there are no action sequences or othertiming-dependent mechanics. The game is won by findingthe target person. While there might be some animationsignaling e.g. player movement, most of the game interfaceis based on structured text, menu choices and static images.3. IMPLEMENTATIONThe game is implemented in Java using libGDX1, a frame-work for multi-platform game development. It currently usesDBpedia and OpenStreetMaps. The former provides datafor generating the plot of the game, while the latter is usedto render maps. Currently, the main mechanics are talkingto NPCs and traveling from one location to another.The game’s plot is defined via one of the possible relationsbetween two real people. With these people as arguments,a path between them is traced using Wikipedia data gath-ered from DBpedia, a community project aiming to extractstructured information from Wikipedia2. For instance, ifthe initial person is Alan Turing and the target person isNikola Tesla, one of the possible paths is shown in Figure 1.Algorithmically, the path is represented as a directed graph,where each person, location, category, etc. is a node andtheir relations are the edges.Figure 1 could be read as: Alan Turing was influencedby Ilya Prigogine, who also influenced Friedrich Har-togs. Hartogs was a mathematician who committedsuicide, as did Ludwig Boltzmann. Boltzmann was afellow of the American Mathematical Society, as is1http://libgdx.badlogicgames.com/2http://dbpedia.org/Figure 1: One of the possible paths between Alan Turing and Nikola Tesla, discovered via the DBpedia crawler.origin 〈P〉 goalorigin 〈P1〉 object 〈P2〉 goalorigin 〈P1〉 object1 〈P2〉 object2 〈P3〉 goalFigure 2: Possible relations discovered by the crawler.queries ← all queries between(A,B);paths ← search dbpedia(queries);sort paths by diversity;path ← best in paths;initialize final path;for i← 0 to path.size− 1 doqueries ← queries between(path[i],path[i+1]);loc paths ← search dbpedia(queries)sort loc paths by diversityloc path ← best in loc pathsadd loc path to final path;endreturn final pathAlgorithm 1: Crawler pseudo-code (A: origin, B: goal).Lloyd Shapley, who was inspired by John von Neu-mann, a prodigy in many fields, like mathematics, languageand memorization. Both von Neumann and Nikola Teslawere gifted, as Tesla had eidetic memory.A crawler has been implemented to search for possible re-lations between two given subjects: the origin and the goal.It is heavily inspired by RelFinder [8], a visualization andexploration tool for web-semantic data. Our search is doneby querying the DBpedia SPARQL endpoint. Several differ-ent queries are generated between the two main subjects, inorder to find links connecting both of them. Figure 2 showspossible relations as responses to the queries, where Pi isthe predicate that joins two subjects (e.g. “subject”, “residein” or “spouse”). Afterwards, one of the resulting paths ischosen and a new search is made for each pair of relationsin this path. Algorithm 1 shows the pseudocode for this.Selecting the best path is made according to the unique-ness of the path. There are multiple ways to relate twodifferent subjects; for instance, two people may have bothlived in the Austrian Empire, but so did millions of others.These two people may also have more specific things in com-mon: a mutual acquaintance, an institution they both wentto or a similar field of research. These less obvious choicesare less general and can possibly create a more interestingplot, and intuitively better clues for the player. To measurethis, we calculate the frequency of each predicate and nodein all possible paths. A path is considered ‘more’ uniquethan another if its edges and nodes appear fewer times inFigure 3: Screenshots from the game. Top: the map ofManchester rendered with OpenStreetMaps. Buildings arepositioned at random locations. Bottom: A part of a dia-logue with a random NPC.the search. For example, suppose a search finds 50 paths,where an edge tagged with “influenced by” appears 80 times(it can appear multiple times in a single path), another with“residence” shows up 20 times, and “work institutions” 15. Ifpath A consists of edges “influenced by” and “residence” andpath B contains edges “residence” and “work institutions”,path B is ‘more’ unique than A, because its edges appearedless in the search results. Path length is also taken in con-sideration: longer paths result in more clues which need tobe discovered and longer gameplay in general. The totalnumber of edges and nodes in the path is added (with ap-propriate weighting) to the path’s uniqueness score.Each node found in the final path is expanded and trans-formed into a NPC, a location or a clue. The current versionof the system does not contain objects, so all clues come inthe form of dialogue lines. NPCs and locations are generatedin a more straightforward manner than clues. People are in-stantiated as NPCs, and their personal characteristics (e.g.age, race, hair color, etc) are included in the object. Loca-tions are created as either cities or buildings within them —states are treated as cities to simplify the process.Clues, on the other hand, are anything that can not betransformed into a NPC or a location (e.g. categories). Foreach clue, a small piece of text from its DBpedia resourcepage is gathered and added as a dialogue line in the NPCs’dialogue tree.In the current state of the project, the game renders im-ages in two manners. For NPCs and buildings, it uses ahandcrafted database of faces and buildings to provide vi-sual information. It is not identifying real images from peo-ple (e.g. it does not use a photo from Alan Turing for hisNPC), nor does it use a real image of the building itself.Maps, however, are rendered using JMapViewer, a JavaSwing component that renders OpenStreetMap (OSM) mapsdata3. OSM is a community-based world mapping project [7].The image is exported as a texture and stored for in-gameuse: an image of the world map is rendered and saved first,then one zoomed map for each city in the game. The coordi-nates of each city are acquired from DBpedia. Screenshots ofa city map and a dialogue in the game are shown in Figure 3.4. OUTLOOKIn this paper, we propose the use of open data to gen-erate content for an adventure game. We use data fromWikipedia, obtained through DBpedia, to create the game’splot, NPCs and locations, and render the game’s maps us-ing OpenStreetMaps. This project is very much a work inprogress. A next step is to obtain the images of people andbuildings automatically (see Figure 4), possibly via APIssuch as those provided by Google. Improving the plot gener-ation is also imperative. At the moment, the story is linear,but adventure games usually require some sort of branch-ing. We can achieve this with false clues, with dead-endlinks from DBpedia, with side-quests (e.g. with another tar-get person) or with multiple paths towards the main plot’starget person. We also intend to add objects to the game:at first only readable objects (e.g. books, letters), and even-tually also objects that can provide different interactionswith the environment (e.g. keys that unlock certain rooms,flashlights that allow to search in a dark place), or puzzles.AcknowledgmentGabriella Barros acknowledges financial support from CAPESand Science Without Borders program, BEX 1372713-3.5. REFERENCES[1] G. A. B. Barros and J. Togelius. Balanced civilization mapgeneration based on open data. In Proceedings of the 2015IEEE Congress on Evolutionary Computation, 2015.[2] C. Browne. Automatic generation and evaluation ofrecombination games. PhD thesis, Queensland University ofTechnology, 2008.[3] A. B. Cardona, A. W. Hansen, J. Togelius, and M. G.Friberger. Open trumps, a data game. In Proceedings of theInternational Conference on the Foundations of DigitalGames, 2014.[4] M. Cook and S. Colton. Multi-faceted evolution of simplearcade games. In Proceedings of the IEEE Conference onComputational Intelligence and Games, 2011.3http://wiki.openstreetmap.org/wiki/JMapViewerFigure 4: Finding data from different data sources to createmaps and characters in the game from a subpath of Fig. 1.[5] M. G. Friberger and J. Togelius. Generating interestingmonopoly boards from open data. In Proceedings of theIEEE Conference on Computational Intelligence andGames, 2012.[6] M. G. Friberger, J. Togelius, A. B. Cardona, M. Ermacora,A. Mousten, M. Møller Jensen, V.-A. Tanase, andU. Brøndsted. Data games. In Proceedings of theInternational Conference on the Foundations of DigitalGames, 2013.[7] M. Haklay and P. Weber. Openstreetmap: User-generatedstreet maps. Pervasive Computing, 7(4):12–18, 2008.[8] P. Heim, S. Lohmann, and T. Stegemann. Interactiverelationship discovery via the semantic web. In Proceedingsof the 7th Extended Semantic Web Conference, volume6088 of LNCS, pages 303–317. Springer, 2010.[9] T. S. Nielsen, G. A. B. Barros, J. Togelius, and M. J.Nelson. General video game evaluation using relativealgorithm performance profiles. In Applications ofEvolutionary Computation, pages 369–380. Springer, 2015.[10] N. Shaker, J. Togelius, and M. J. Nelson. ProceduralContent Generation in Games: A Textbook and anOverview of Current Research. Springer, 2015.[11] J. Togelius and M. G. Friberger. Bar chart ball, a datagame. In Proceedings of the International Conference onthe Foundations of Digital Games, 2013.[12] J. Togelius, M. J. Nelson, and A. Liapis. Characteristics ofgeneratable games. In Proceedings of the 5th Workshop onProcedural Content Generation in Games, 2014.[13] J. Togelius and J. Schmidhuber. An experiment inautomatic game design. 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Data adventures

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Data adventures

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