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  • Data Adventures

    Gabriella A. B. BarrosCenter for Computer

    Games ResearchIT University of Copenhagen

    Copenhagen, Denmarkgbar@itu.dk

    Antonios LiapisInstitute of Digital Games

    University of MaltaMsida, Malta

    antonios.liapis@um.edu.mt

    Julian TogeliusDepartment of ComputerScience and Engineering

    New York UniversityNew York, USA

    julian@togelius.com

    ABSTRACTThis 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 games non-playable characters locations and plot, andOpenStreetMaps to create the games 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 [13].

    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? (BrderbundSoftware 1985), as a challenging novel domain of game gen-eration methods. This poses new challenges for content gen-eration and game generation. In particular, the generatedneeds to be meaningful in terms of its textual descriptionas 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 within

    cities. 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 [7]. 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 some game genres and an active research field [11].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 [13].

    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 [14,5, 10]. 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 [13].

    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 on

  • so 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 [7, 4]. 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 [12], 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 [6], 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 use docreate 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 adventures 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 andunexpected associations between pieces of information orunlikely objects. The core challenge is the discovery 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 hintsprovided by NPC dialogue. Challenges in most adventuregames are local: a player must solve a puzzle in order forthe story to move forward and the next puzzle be presented.Some adventure games can include losing conditions, wherea player needs to load or restart from a previous game state,although more often players can always revisit all previousstates of the game and search for missing clues. Due to thecarefully written end-game and plot progression, most ad-venture games are played once and have little replay value.

    In this project, we are particularly inspired by Where inthe world is Carmen Sandiego?, a series of very popular edu-cational adventure games by Brderbund 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 Sandiegosappearance 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 kind of data game.

    2. PROPOSED GAME DESIGNTraditionally, creating an adventure game is a laborious

    process 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 NPCs dialogue. Fundamentally, the players 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 exploresites in each city. Entering sites within a city, the playercan interact with NPCs and objects found there in order toget clues which can unlock new locations, NPCs or dialogueoptions. Some objects or NPCs might give false clues, andNPCs can give no clues at all. A clue can be either a line ofd