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Automated Content Generation

Automated Content Generation. Dennis Dedaj HAW Informatik Master Anwendungen 2. Agenda. Introduction Pervasive Role Play Game Motivation What kind of content? Technologies for Automated Content Generation Map Fragmentation Content distribution. Pervasive Gaming.

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Automated Content Generation

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  1. Automated Content Generation Dennis Dedaj HAW Informatik Master Anwendungen 2

  2. Agenda • Introduction • Pervasive Role Play Game • Motivation • What kind of content? • Technologies for Automated Content Generation • Map Fragmentation • Content distribution Automated Content Generation

  3. Pervasive Gaming • ‘The world Within’Aspects of PG from [Walt05] • Mobile • Play everywhere • Distributed • Player versus Player • Persistence • The world is always available • Transmediality Automated Content Generation

  4. Motivation • Pervasive Gaming Project • Pervasive Role-Play-Game ‘The World Within’ • Avatars life in an ‘empty’ virtual reality • No time to populate the virtual reality • No motivation to populate it manually • But motivation to populate it automated • AW 1 continued… ‘Game Engineering’ • Authoring tools Automated Content Generation

  5. What kind of content? • Buildings • Hotels, ruins, supermarkets, tool shops, bars … • Breed centres • Lakes, rivers, caves … • Opponents (NPC) • Stories • Paths through the world annotated with narrative content • Quests / Player-Tasks • Generation of puzzles Automated Content Generation

  6. Technologies forAutomated Content Generation • Map Fragmentation • Geographic Information Systems - GIS • ColourMaps • Content distribution • Cellular Automata • Agents • Simple Random Automated Content Generation

  7. Map Fragmentation • Recognize map areas for content to be generated • Define task/quest/story paths • Mark unreachable areas • Detect environmental parameters Automated Content Generation

  8. Geographic Information Systems • Relative ‘new’ technology • Public interfaces under development • KML [GoEa09] • GML [OpGe09] • Huge amount of data … • climate / earth related • united states and canada • Less regional and local data • granularity • coverage • Do those maps know what fun is? Automated Content Generation

  9. ColourMapsMartin Flintham, University of Nottingham [Flin05] • Authoring of Location-Based Games by simple colouring of physical game maps • Association of Location-Based triggers with areas of a physical map • Quick configuration of new physical areas • Live orchestration of games in real-time • Dealing with the inherent uncertainty of GPS Automated Content Generation

  10. ColourMaps • Can You See Me Now? [CaYo08] • Mixed Reality Game • Goal: Runners have to catch all Players • Content: Cities with Buildings Automated Content Generation [BeMa05]

  11. ColourMaps • Game Designer defines Start Positions • Server searches Map for Start Positions • When a player enters the game a random position will be assigned • Easy weighting • Easy modifying [Flin05] Automated Content Generation

  12. ColourMaps • Game Designer defines accessable areas • Position of runners will be filtered • For example:Mapping a forbidden point to the nearest point outside [Flin05] Automated Content Generation

  13. ColourMaps • Uncle Roy All Around You • Goal: Players have to find Uncle Roy • Content: Cities with clues for defined areas • While Player move through cities the system gives clues depending on the position of the player Automated Content Generation

  14. ColourMaps Clue Trails • Game Designer walks through physical area, writes clues and associates a colour [Flin05] Automated Content Generation

  15. ColourMaps Clue Trails • Game Designer walks through physical area, writes clues and associates a colour • Players move through physical or virtual areas • Each movement into a different ‘clue area’ throws an event [Flin05] Automated Content Generation

  16. ColourMaps Multilevel Clue Trails • Player moves into the same area again, a different clue is given [Flin05] Automated Content Generation

  17. ColourMaps - Overview • Content regions defined in a xml file • Multiple layers offer flexibility Automated Content Generation [Flin05]

  18. Map Fragmentation • Trade-Off • ColourMaps • fast mediation, game designer know the tools • not completely automated • full immersion control • Geographic Information System • higher complexity • inaccurate • completely automated Automated Content Generation

  19. Content distribution • Simple random algorithms • Cellular Automata • Agents Automated Content Generation

  20. Simple random algorithms • Pro • no complexity • fast generation • Contra • lesser fun • continous immersion [MaNo05] Automated Content Generation

  21. Cellular Automata • Pro • very different generation • probably more fun • various immersion • Contra • can be complex • long computation time Automated Content Generation [MaNo05]

  22. Agents • Pro • realistic generation • probably more fun • various immersion • Contra • can be complex • long computation time • complex definition Automated Content Generation [MaNo05]

  23. Vision / Ideas • Cellular Automata • Instancing opponents • More dimensions • O(n)-complexity • Agents • Instancing buildings • Looks for hotels and other real world entities • User mediated content • Stories • Paths • Instantiation depends on amount of users at specific places Automated Content Generation

  24. Bibliography Automated Content Generation

  25. Vielen Dank für die Aufmerksamkeit! Automated Content Generation

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