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The intelligent game designer: Game design as a new domain for automated discovery

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Designing video games is commonly understood to be a creative task, ...

Designing video games is commonly understood to be a creative task,
drawing on a designer's talent, inspiration, and personal experience.
The last ten years have seen multiple calls from the design community to
produce reusable knowledge about the structure of games and the design
process itself. These designers would like to establish a standardized
language and libraries of design patterns so that the next generation of
designers can benefit from the best of past generations. The
realization of such a move can be read as a transition from thinking
about game design as a playable-artifact creation process to a science
of play in which we might see the designer's goal as discovering new
gameplay structures and their production of concrete games as a side
effect of this process.

Thirty years ago, a similar-yet-disconnected thread of research in
artificial intelligence was just being born. First marked by Doug
Lenat's AM (an “automated mathematician”), discovery systems aim to
automatically produce new and interesting knowledge. Such systems
contrast sharply with the then-popular expert systems which applied
fixed libraries of “expert” knowledge to various tasks. Discovery
systems, which have commonly operated in the domains of natural science
and mathematics, are now seen as distant ancestors of contemporary,
statistical machine learning techniques which find extensive application
in a wide array of industries. Contrary to the current emphasis on the
optimal learning statistical descriptions of data, some recent
developments in machine learning, specifically combined abductive and
inductive logic learning systems, are bringing the production and
revision of structured, symbolic knowledge back into focus.
Simultaneous research in computational creativity is making inroads into
modeling the creative process and the production of creative artifacts.

This is the question I aim to answer: If we squint a bit to see game
design as the science-of-play that some designers imagine it to be, can
we build a discovery system that really works in the domain of game
design? Can we build an intelligent game designer?

In my thesis proposal I lay out a plan to build an intelligent game
designer that learns from the process of game design, including the
observation of human players, and exports newly discovered design
knowledge. This will require an operationalization of game design as an
automatable, scientific process and a detailed re-synthesis of the
creative design of expressive artifacts as a knowledge-seeking effort.

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  • Full Name Full Name Comment goes here.
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  • Very cool idea.Thank for your presentation with inovative ideas.

    http://www.natural-cures-ebooks.com
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  • Really interesting idea!! Good luck.
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  • Continue the discussion here: http://eis-blog.ucsc.edu/2009/07/game-design-as-a-science/
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The intelligent game designer: Game design as a new domain for automated discovery Presentation Transcript

  • 1. The Intelligent Game Designer:Game Design as a New Domain for Automated Discovery
    amsmith@cs.ucsc.edu
    29 July 2009
    Adam M. Smith
  • 2. Preface
  • 3. Preface
    Game design is clearly a creative activity.
    I claim a machine can do it.
  • 4. Preface
    Bruce Buchanan (in AAAI-2000 Presidential Address)says of existing creative systems…
    they do not accumulate experience, and thus, cannot reason about it;
    they work within fixed frameworks including fixed assumptions, and criteria of success;
    they lack the means to transfer concepts and methods from one domain to another.
  • 5. Preface
    My “intelligent game designer” is all about turning experience into communicable knowledge (producing games along the way).
    But how?
    Operationalize game design as an automatable scientific process.
    Re-conceptualize creative design of expressive artifacts knowledge-seeking effort.
  • 6. Why is this realistic?
    Why me?
    Game development
    Generative art
    Why now?
    Fresh tools
    Abductive/Inductive logic learning
    Automated debugging for logic programs
    Fresh formalisms for games
    Event-calculus
    Recombinable mechanics
  • 7. Introduction
    Context
    Research Questions
    Proposal Outline
  • 8. Perspectives in Game Design
    Experience sharing (informal knowledge, words)
    Textbooks, forum posts, and technical talks
    Code Sharing (formal knowledge, code)
    Procedural content generation, drama management, game engines, and miscellaneous middleware
    Nearly-automated Systems
    Peer or design buddy?
  • 9. Perspectives in Learning / Creativity
    Statistical ML / Computational Intelligence
    Structured data in, predictive model out
    Discovery systems
    Data must be drawn out by experiment
    Predictions should be consistent with rich, domain-specific, background knowledge
    Creative art systems
    Artifacts are like exquisite experiments, results ignored
    Leverage highly nuanced audience model, often fixed
    Domain-aware, creative discovery systems
    Learning is the focus, artifact creation as side-effect
  • 10. Research Questions
    Function:
    How does an intelligent game designer function?
    Implication:
    What does such a system imply for the relationship between discovery and expressive domains?
  • 11. Function: “games”
    Recognizable as “video games”
    Focusing assumptions:
    Single-player
    Real-time
    Mechanics-heavy
    Abstracted representation
    Minimal setting
  • 12. Example “game”: Dyson
    “Remotely command semi-autonomous self-replicating mining machines to take over an entire asteroid belt.”
    • Single-player
    • 13. Real-time
    • 14. Mechanics-heavy
    • 15. Abstracted representation
    • 16. Minimal setting
    http://www.dyson-game.com
  • 17. Function: “intelligent”
    Learning from experience
    Knowledge production as a function of past design and discovery actions
    Documentation as proof
  • 18. Function: “game design”
    Game design: the informed construction of rules systems and supporting logic required to produce playable games
    OK if playable games are a little rough, some human polish might be needed
  • 19. Implications: Game Design
    What does _____ mean in game design?
    Discovery?
    Conjecture?
    Experiment (environments, observations, instruments)?
    Verification?
    Proof?
  • 20. Implications: Discovery
    What does _____ mean in discovery?
    Prototyping and play testing?
    Publishing a game?
    Games vs. abstract state progression systems?
    Expressive goals?
    Fun?
  • 21. Research Questions Revisited
    Function:
    How does an intelligent game designer function?
    Need to build a system!
    Implication:
    What does such a system imply for the relationship between discovery and expressive domains?
    Need some theories to generalize!
  • 22. Outline
    Related work
    Game design
    Discovery and creativity systems
    Prior work
    Interactive generative art
    Logical games
    Elementary discoveries in game design
    Proposed work
    Theories
    Systems
    Experimental validation
    Time line
  • 23. Related Work
    Textbook game design
    A call for structure
    Game studies
    Artificial intelligence
    Models of discovery
    Discovery systems
    Computational creativity
    Generative art
  • 24. Artifacts
    Design documents
    Prototypes
    Paper
    Computer-assisted
    Computational
    Complete games
    Processes
    Concept development
    Design
    Prototyping
    Play testing
    Self-testing
    Testing with friends
    Testing with target audience
    Tuning
    Marketing
    Textbook Game Design
  • 25. A Call for Structure
    “Not enough is done to build on past discoveries, share concepts behind successes, and apply lessons learned from one domain or genre to another.” – Doug Church
    Formal Abstract Design Tools (Church 1999)
    400 Project(Barwood 2001)
    The Case for Game Design Patterns (Kreimeier 2002)
  • 26. Game Studies
    Swap Adjacent Games to Make Sets of Three (Juul 2007)
    Patterns in Game Design (Bjork 2005)
    Game Ontology Project (Zagal 2005)
  • 27. AI: Game Generation
    EGGG(Orwant 2000)
    Automated Puzzle Generation(Colton 2002)
    Towards Automated Game Design (Nelson 2007)
    An Experiment in Game Design (Togelius 2008)
    Rhythm-Based Level Generation for 2D Platformers(Smith 2009)
    Q. Jingle is to corporation as ___ is to politician:
    (a) campaign (b) platform (c) slogan (d) promises
    A. Slogan.
    Q. What is the next in the sequence: 3, 8, 15, 24, 35?
    A. 48: Starting from 2, square each consecutive integer then subtract 1.
  • 28. AI: General Game Playing
    GGP: getting machines to play arbitrary games well given only the rules and a little bit of time to practice (evolved from AI chess)
    Game Description Language (Love 2006) describes games as state transition systems in datalog.
  • 29. AI: Game Design Assistance
    Parallel research by Mark J. Nelson at EIS
    Goal: create a game-design assistant that helps designers prototype their rule systems
    Gist:
    Let the machine comment on formal issues
    Reachability, exploits, indirect constraints
    Let human players comment on soft issues
    Engagement, fun, hesitation
  • 30. Personal Game Design Experience
    Drive-by CTF
    AjaxWar
    KatamariDamacy Text Adventure
    the.cubing.game
    the.discrete.gardender
    Sequence Sleuth
    Troy
    fusepuck
    T++others I’ve forgotten…
  • 31. Two common domains:
    Natural science
    physics, chemistry, genomics, virology
    Mathematics
    graph theory, number theory
    Two common goals:
    Explain historic discoveries
    Produce new knowledge
    Unifying vocabulary for discovery: (Shrager and Langley 1990)
    Knowledge structures
    Processes
    Models of Discovery
  • 32. Early Systems
    Discovery Systems
    DENDRAL (Feigenbaum 1965)
    AM (Lenat 1977)
    Refinements
    BACON (Langley 1977)
    EURISKO (Lenat 1985)
    CYRANO (Haase 1987)
    Graffiti (Fajtlowicz 1988)
    GT (Epstein 1988)
    Modern components
    Abductive and inductive logic learning
    Inductive process modeling
    Statistical-relational learning
    HR (Colton 1999)
  • 33. Theoretical Models
    Conceptual spaces (Boden)
    Domain, individual, field, interaction (DIFI) (Feldman)
    Curiosity (Saunders)
    Perceptual Creativity (Colton)

    Aspects
    Artifacts
    Processes
    Expectation
    Emotion
    Socialization
    Novelty and value
    Generate and test loop

    Computational Creativity
  • 34. Creative Art Systems
    AARON (Cohen)
    NEvAr(Machado)
    Digital Clockwork Muse (Saunders)
    EMI (Cope)
    MINSTREL (Turner)
    The Painting Fool (Colton)
  • 35. Recap of Related Work
    Game Design
    Textbook + Call for more structure
    Game studies + AI
    Discovery and Creativity
    Models of Discovery + Systems
    Computational Creativity + Systems
  • 36. Prior Work
    Tableau Machine
    Logical game design
    Game generation
    Elementary discovery in game design
  • 37. Tableau Machine
    Experience formalizing an expressive domain
    Generate-and-test
    Design grammars
    Image analysis
    Learn long-term patterns in sensor data to stay relevant
  • 38. BIPED: Computational support for play testing game sketches
  • 39. Example Game: DrillBot 6000
    happens(mine(a1),0).happens(drain,1).happens(drain,2).happens(trade,3).happens(mine(a2),4).happens(mine(a0),5).happens(down_to(a),6).happens(mine(corpse),7).happens(mine(c0),8).happens(down_to(c),9).happens(down_to(f),10).happens(up_to(c),11).happens(up_to(a),12).happens(down_to(c),13).happens(down_to(f),14).
  • 40. Logical Game Programming
    Movement mechanic from DrillBot 6000
    pos(base). pos(a). pos(b). pos(c). …
    game_state(position(P)) :- pos(P).
    game_event(up_up(P)) :- pos(P).
    game_event(down_do(P)) :- pos(P).
    initiates(down_to(P),position(P)) :- pos(P).
    terminates(down_to(_),position(Prev)) :- holds(position(Prev)).
    initially(position(base)).
  • 41. Logical User-Interface Programming
    UI bindings in DrillBot 6000
    ui_title(’DrillBot 6000’).
    ui_space(P) :- pos(P).
    ui_space(inventory).
    ui_token(db6k).
    ui_location(db6k,P) :- holds(position(P)).
    ui_triggers(ui_click_space(P),down_to(P)).
    ui_triggers(ui_click_space(base),refuel).
  • 42. Capabilities
    Syntactic properties
    Design validation
    Semantic properties
    Trace harvesting
    Rule set debugging
    Win-ability verification
    Reachability analysis
    Uniqueness verification of puzzle solutions
    Testing a game before you ever make a UI
    Induction on semantics
    Player-model construction
  • 43. Game Generation
    BIPED-tech is great for testing game ideas, but who generates them in the first place?
    Need a “design grammar” for games
    Propositional game generator experiment
    Generation of rule systems is feasible.
    Needs higher-level building blocks:
    Multi-clause rules
    Multi-rule mechanics
    Higher-level mechanics
  • 44. A general “network navigation” design pattern at the code level
    “Movement between underground caverns”in Drillbot 6000
    % positions (caverns)
    pos(base).
    pos(a).
    pos(b).
    pos(c).
    % links (drillable routes)
    link(base,a).
    link(a,b).
    link(a,c).
    % event preconditions
    possible(down_to(Dst)) :- holds(position(Src)), link(Dst,Src).

    Setting: predicate room(R)
    State: location(R) such that room(R)
    Setting: doorway(R1,R2) such that room(R1) and room(R2).
    Event: move_to(R) is possible only if room(R) and you location adjacent room, as judged by doorway

    Elementary Discovery in Game Design
  • 45. Recap of Prior Work
    Tableau Machine
    Logical game design
    Game generation
    Elementary discovery in game design
  • 46. Proposed Work
    New Theories
    System Architecture
    Experimental Validation
    Timeline
  • 47. Reviewing the Knowledge Level
  • 48. A Game Design Meta-Theory
  • 49. A Knowledge-Level Account of Game Design
    Agents
    Designers!
    Actions
    Game actions
    Play actions
    Design actions
    Goals
    Discovery of design-level knowledge
  • 50. Reflexive Creativity
    My conjecture on creativity:
    Creativity is the rational pursuit of curiosity that results in a surprise.
    Mash up some theories:
    If game designer aim to discover…
    And discovery is way to satisfy curiosity…
    Maybe creative game designers make games to help them discover!
  • 51. System Overview: Exterior
  • 52. System Overview: Interior
  • 53. Symbol-level Implementation
    Implement as a production system
    Facts, rules, and a rule engine/executive
    Operational knowledge:
    Fixed rule set
    Design theory:
    Mutable rule set, Mutable fact-base
    Artifact library:
    Append-only fact-base
    Discovery Notebook
    Mutable fact-base
  • 54. Operational Knowledge
    Remember those scientific knowledge structures and scientific processes? I’ve translated them to game design.
    Layered mapping:
    Scientific knowledge structures and processes
    Game design knowledge structures and processes
    Data structures and operations
    Production rules and facts
  • 55. Artifact Library
    % entry for an generated sequel to DrillBot 6000
    game(db6k_mk2, [
    construction(expand_map(drillbot6000)),
    rules({BIPED-compatible rule set}),
    design_annotations(expand_map_seed(12391))]).
    % a player model
    player(energy_hog, [
    construction({prodution rule used to produce this player}),
    rules({internal state, predicate transformers, BIPED-compatible play-hook clauses}),
    design_annotations({…})]).
    % entry for an instance of play
    play_instance(pairing23423,[
    game(db6k_mk2),
    player(energy_hog),
    pairing_rules({choices the system had to make to glue the player to the game}).
  • 56. Design Theory
    What-is knowledge
    Design patterns (named and detailed game and play structural elements)
    Recall the “network navigation” pattern
    Trace predictors
    “If the game contains pattern X, then Y should be found in the trace”
    How-to knowledge
    When-to-always and when-to-never perform certain design actions under certain conditions
  • 57. Raw Design Theory Examples
    % a movement mechanic
    mechanic(movement_between_rooms(R,D),[
    dungeon_map(R,D),
    game_state(at(agent/1,R)),
    game_event(moves_to(agent/1,R)),
    {trigger logic}
    ]).
    % player’s view of the game state as a player character
    player_construct(pc_avatar(PcPred),[
    binder(pc_avatar(PcPred)),
    pc_avatar(Pc),
    pp_mapper(in(Pc,X),out(X))]).
    % trace property predictor
    trace_implication(happens(victory,T2),happens(boss_kill,T1)) :-
    T2 <= T1, mechanic(boss_kill_victory).
    % how-to
    designers_never(game_apply(expand_map),game_apply(expand_map)).
  • 58. Discovery Notebook
    Starts empty
    Contains
    Outstanding experiments
    Expectations
    Agenda

    General working memory
    Usage dictated by operational knowledge
  • 59. Actions:
    Design actions:
    Manipulate game, player, and play instance models
    Solicit a game or play trace from an automated tool or a human player
    Discovery actions:
    Propose new game, player, and play instance structural elements or production constraints
    Look for examples of new patterns in old artifacts
    Verify (prove?) trace predictors
  • 60. Design Tools
    Design validator
    Gameness-check
    Trace-finder
    Human player trace harvester
    Logical debugger
    Misc. statistical-relational learning tools
    Potential add-ons from Mark’s research:
    Alternate trace-finding back-ends
    Query suggester and answerer
    Rule visualizer
  • 61. Experimental Validation – Q1
    How does an intelligent game designer function?
    “games” – Ask some players if the machine-designed games felt like real games
    Do the players think the games feel real?
    “intelligent” – Ask some expert designers to perform some discovery, and record the result (using same tools).
    Does my system rediscover it?
    Did my system discover something beyond it?
    “game design” – Ask some expert designers to design some games and record the result (using same tools).
    Does the designer think the games feel real?
    Does the system produce the same kind of games?
  • 62. Experimental Validation – Q2
    What does such a system imply for the relationship between discovery and expressive domains?
    Test the theory by testing the systems designed according to it.
    Toggle various elements of the architecture to see what is really to blame for the interesting behavior
    Need implications in-hand to propose concrete experiments
  • 63. Time Line
    Year one: focus on stretching game design into a science-like practice, automation comes at the very end
    Summer 2009: play with more manual discovery
    Fall 2009: implement scientific knowledge structures
    Winter 2010: implement the scientific processes
    Spring 2010: integrate the system, close the loop
    Year two+: focus on architectural experimentation, system evolution, and generalizing to my theoretical contributions
    Summer 2010: plan the dissertation
    Fall 2010: perform the experiments
    Winter 2011: synchronize experimental results with plan
    Spring 2011: dissertation writing
    Summer 2011: final polish and defense
  • 64. Recap of Proposed Work
    Improve upon some new theories
    Implement system according to proposed architecture
    Validate my intelligent game designer in experiments
    Generalize from working system to more general theories
  • 65. EPILOGUE
    Revisiting Buchanan’s criticisms
  • 66. Epilogue
    Bruce Buchanan (in AAAI-2000 Presidential Address)says of existing creative systems…
    (1) they do not accumulate experience, and thus, cannot reason about it;
    (2) they work within fixed frameworks including fixed assumptions, and criteria of success;
    and (3) they lack the means to transfer concepts and methods from one domain to another.
  • 67. Thanks
    Thesis proposal: PROPOSED
  • 68. Detailed First Year Plan
    Summer 2009
    Flesh out first system architecture (mostly complete)
    Perform manual discovery with the raw tools and representations (already started)
    Produce example outputs
    Document the manual process
    Fall 2009
    Implement knowledge structures
    Games, player, play instances, trace predictors
    Structural elements (setting constructs, mechanics, player predicate transformers)
    Perform manual discovery with richer representations
    Winter 2010
    Implement processes
    Drivers/scripts for external tools
    Action sequences (“get a trace, then induce a player model”)
    Heuristic processes selection (“try verifying a trace prediction”)
    Perform manual discovery using large-scale processes as the individual move
    Spring 2010:
    Write up preliminary view of game design as a scientific domain (with structures and processes in-hand)
    Plan expert and player evaluations
    Create minimal closed-loop system
    Theory goes in, improved theory comes out; also, games were produced
    Improve system by building larger scale processes
  • 69. Detailed Second Year Plan
    Summer 2010
    Write up initial results and architecture of integrated system
    Perform final literature review
    Game design, discovery, and generation in expressive domains
    Digital media (and other fields outside my own) for reference on expressive artifacts
    Formulate initial implications between discovery and expressive domains
    Design experiments to test implications
    Produced detailed dissertation outline
    Fall 2010
    Carry out experiments
    Winter 2011
    Reconcile experimental results with theory, adjust claims
    Start dissertation writing
    Spring 2011
    Dissertation writing
    Clarifying experiments
    Summer 2011
    Final polish and defense