mechanizing exploratory game design (phd defense)
TRANSCRIPT
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expressiveintelligencestudio
UC Santa Cruz
Mechanizing Exploratory Game Design
Adam M. Smith
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MECHANIZATIONMusing on
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expressiveintelligencestudio UC Santa Cruz
Mechanized …
mechanized agriculturelabor reduction
mechanized warfareforce multiplication
mechanized reasoningabstraction
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Bicycles for the mind
“What a computer is to me is the most remarkable tool that we have ever come up with. It’s the equivalent of a bicycle for our minds.” -- Steve Jobs
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Not a fancy bicycle
More like a wheel Standard component with which to build tools Fundamentally changes the mechanics of friction
Three example vehicles
Reference theory & practice for building more
This dissertation
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AGENDADissertation Defense
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Motivation Research Goals Background Theory Systems Evaluation Conclusion
Agenda
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MOTIVATION
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“The goal of game design is meaningful play, but play is something that emerges from the functioning of the rules. As a game designer, you can never directly design play. You can only design the rules that give rise to it. Game designers create experience, but only indirectly.”
Games, interactivity, and indirection
Salen & Zimmerman 2003
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Unfolding in branching time
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Exploratory game design
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Conventional designin familiar territory
Exploratory designin unfamiliar territory
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I’m a designer-programmer. I’m into exploratory game design. What do I want?
Tools for exploration
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Faster/cheap/better feedback
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Crafting during play
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Design assistants
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There is a technical and practical intervention that advances each of these desires simultaneously.
Intervention
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RESEARCH GOALS
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Amplify human game designer creativity What is creativity? What is it after? How do you amplify it?
Support deep, play-time design automation Which parts of design can be folded into play? Can machines
carry out the required activity? How?
Demonstrate tools that respect design problems What is a design problem? How can a tool be receptive to a
design problem? What does a design tool do?
Research goals
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BACKGROUND
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Game design Design studies Computational creativity Symbolic AI
Interdisciplinary context
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Game design
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Prototyping: get feedback on design idea without building full
games Playtesting:
bring game into contact with players to expose what the designer is trying to design: play
Game design: Prototyping & Playtesting
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Game design: Content generation (PCG)
Ludi (game generator)
Yavalath (generated game)
Infinite Mario Bros!
Tanagra
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Design studies
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expressiveintelligencestudio UC Santa Cruz
Design studies: the “third culture”Culture Phenomenon of
studyAppropriate methods Values
Sciences The natural world Controlled experiment, classification, analysis
Objectivity, rationality, neutrality, and a concern for ‘truth’
Humanities Human experience Analogy, metaphor, evaluation
Subjectivity, imagination, commitment, and a concern for ‘justice’
Design The artificial world
Modeling, pattern-formation, synthesis
Practicality, ingenuity, empathy, and a concern for ‘appropriateness’
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Wicked problems Ill-definedness Primary generators Solution leading/focusing Reflective practice Problem framing/construction Double-loop learning Situational backtalk Satisficing Appropriateness Abductive/Appositional reasoning
Design studies: Vocabulary
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Design studies: Metacircularity
Jones (1979) Brooks (2010)
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Computational creativity
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… … Demonstrate artifact generators in human-
dominated creative domains Extreme generalization of PCG for games
Computational creativity: Goal(s)
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Symbolic AI
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Discrete, named objects (symbol) vs. indistinct aggregates (int) vs. continuous flows (float)
Symbolic AI: Symbols
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Relations between objects south_of(santa_cruz,seattle)
Truth, derivations, choice, preference city(santa_cruz). expensive(C) :- avg_home_price(C,P), P > 500000. { overcast }. #minimize { in_basket(Item):cost(Item,C)=C }.
Symbolic AI: Symbolic logic
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Planning Scheduling Theorem proving Constraint satisfaction Integer linear programming Satisfiability checking (SAT solving) …
Symbolic AI: Problems
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Termination and completeness Heuristics Consistency and propagation Constraint learning
Automatically learning implied constraints from common dead-ends in search space
Randomness and restarts
Symbolic AI: Solutions
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Zooming out…
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THEORY
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expressiveintelligencestudio UC Santa Cruz
Design space: the set of artifacts that a designer knows to be
appropriate to their best understanding of a design scenario
Generative space: the set of artifacts a generative process might
foreseeably generate
How do you make these coincide?
Design space vs. Generative space
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Constructivity Expresses complete configuration of artifacts Supports automatic artifact generation
Constraint Expresses form constraints (local/global) Expresses function constraints (exist/forall)
Evolution Supports iterative development (elaboration tolerance) Supports automatic learning and/or repair of constraints
Analysis Supports justification of appropriateness Supports diagnosis of externally specified artifacts
KR Requirements for Design Spaces Models
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Answer Set Programming (ASP) … is a declarative logic programming paradigm
focused on complex (NP and NPNP) combinatorial search and optimization problems.
Pro: derived from KR research high-performance, complete search algorithms
Con: strong symbolic bias
Means of mechanization
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Design automation cycles
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Guess an artifact description so that none of its deduced properties are forbidden, optionally optimizing a metric.
Modeling in AnsProlog
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Which two numbers multiply to 42?Of those, which pair has the smallest sum?
AnsProlog code:% guess a pair of numbers2{ picked(1..42) } 2.% derive their productproduct_ok :- picked(A), picked(B), A*B==42.% forbid lack of correct product:- not product_ok.% optimize (weighted) sum#minimize [ picked(N)=N ].
Modeling in AnsProlog (2)
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SYSTEMS
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Faster/cheaper/better feedback Biped
Crafting during play Variations Forever
Design assistance/assistants Refraction Tools
Systems Roadmap
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Biped
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expressiveintelligencestudio UC Santa Cruz
Human game players are unreliable and expensive, but they provide unique insights
Machine game players are reliable, cheap and customizable, but they are inaccessible to designers
I want a new kind of prototyping/playtesting with the best of both, and it better not require a PhD to use!
Biped: Problem
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Ludocore/Biped: Solution
Queryable formal model
Game idea (in designer’s mind)
Game sketch (in custom logic language)
Human-playable prototype
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Variations Forever
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expressiveintelligencestudio UC Santa Cruz
Games with recombinant rule/content mechanics are interesting, but most are flakey
Can we make a game with variable rules and retain control over emergent results?
Variations Forever: Problem
ROM CHECK FAIL (Farbs 2008)
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Variations Forever: Solution
Flash gameAnswer set solving
web service
Design space model
Mini-game design choices
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Refraction tools
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expressiveintelligencestudio UC Santa Cruz
Given: pre-existing game design
We want to design new levels
We want to understand old levels
We want infrastructure for deeply-generative sequel
Refraction tools: Problem
Refraction gameplay
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Refraction tools: Solution (Puzzle Solver)
Puzzle Solution
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Refraction tools: Solution (Puzzle Generator)
Expressions: “((1/2)/2)”, “(1/2)+(1/4)”, “(1/4)+(1/4)”, Parameters: 24 blockers, 7 benders, 7 distractors
Space-embedded puzzle
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Aside: Controlling emergent style
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EMERGING RESULT!Two-level design spaces
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On a Go board, how can I place 5 black stones so that it takes you 7 white stones to link them into a network?
Two-level design spaces: Problem
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One-level ∃p, r: App(p,r)
Two-level
∃p, r : ( App(p,r) ⋀ (∀a : ¬Regret(p,a) ) )
Logical formalization
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Two-level design spaces: Code
% parameterswidth(5).num_black(5).num_white(7).
% grid setuppos((1..X,1..Y)) :- width(X;Y).step(1;-1,0;;0,1;-1).adj((X,Y),(X+DX,Y+DY)) :- step(DX,DY), pos((X,Y);(X+DX,Y+DY)).
% guess black placementN { black(X) : pos(X) } N :- num_black(N).:- not black((1,1)).
% guess white placement{ white(X) : pos(X) }.:- black(X), white(X).
% deduce if solvedroot((1,1)) :- width(W).sel(X) :- black(X).sel(X) :- white(X).connected(X) :- root(X), black(X).connected(Y) :- connected(X), adj(X,Y), sel(Y).wconn(X) :- connected(X), white(X).solved :- num_black(NB), NB { bconn(X) }.
% deduce if used enough piecesbconn(X) :- connected(X), black(X).enough :- num_white(NW), NW { wconn(X) }.
% deduce designer regretregret :- solved, not enough.
% setup two-level metaproblem#meta conformant(black(X)):pos(X).#meta unsafe(reget).
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EVALUATION
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Practicality: does it work? Ingenuity: is there something new here? Empathy: am I connecting with designer-programmers?
Design evaluationCulture Phenomenon of
studyAppropriate methods Values
Design The artificial world Modeling, pattern-formation, synthesis
Practicality, ingenuity, empathy, and a concern for ‘appropriateness’
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Game design undergrads with no symbolic logic background could learn, use, and extend the tool PhD not required!
“Structural queries” often didn’t require deep model of play Extract ASP as general PCG method
Declarative, interactive graphics were interesting for gameplay prototypes Need to explore up more, mechanize mapping
Biped: Backtalk
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Online integration approach was quite reusable Reused in RoleModel, Anza Island, Refraction, more…
Design space is closely tied to engine, engine not exposing enough control (recurring in Game-o-Matic project) Shifted bottleneck away from ruleset invention We need a new kind of engine for this kind of game!
Surprising dynamics from seemingly well-known mechanics Indirect pushing dynamics:
Variations Forever: Backtalk
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expressiveintelligencestudio UC Santa Cruz
New solver was order of magnitude smaller and quickly returned results for puzzles unsolvable by original tools. Both are great for design; came from AI advances
New solver afforded rich control over the kind of solution to look for Solver is now reliable analysis power tool
Solver is much simpler than actual game but 100% faithful to original mechanics We can use it to prototype new variations!
Refraction tools: Backtalk (Puzzle Solver)
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expressiveintelligencestudio UC Santa Cruz
We care about style, deeply. Continue to expand formal encodings of our
aesthetic concerns
We wanted something other than mission generation and grid embedding. Seek control over all solutions, not just reference
solution
Refraction tools: Backtalk (Puzzle Generator)
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expressiveintelligencestudio UC Santa Cruz
ASP was weak on expressing NPNP problems. Working with Potassco team to disseminate new,
modeling language feature
Most complexity in my past ASP projects was involved in carefully modeling space of play as a deducible property of artifact. Now easy to model maze design without
describing bounded reachability analysis
Two-level design spaces: Backtalk
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CONCLUSION
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expressiveintelligencestudio UC Santa Cruz
I’ve advanced a practical, novel, and accessible design practice.
I have developed a comprehensive and new perspective on the nature of design for interactive artifacts.
Contributions
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![Page 71: Mechanizing Exploratory Game Design (PhD Defense)](https://reader036.vdocument.in/reader036/viewer/2022062319/5589a12fd8b42ad3608b469e/html5/thumbnails/71.jpg)
expressiveintelligencestudio UC Santa Cruz
Two-level design spaces Further develop models and explanations of design
for inherently-interactive artifacts Integration infrastructure
Tighten gap between design spaces and artifact-consuming systems
Broader base of modeling examples Develop reference material and supporting utilities
that bring others up to speed without reading a doctoral dissertation.
Future work
![Page 72: Mechanizing Exploratory Game Design (PhD Defense)](https://reader036.vdocument.in/reader036/viewer/2022062319/5589a12fd8b42ad3608b469e/html5/thumbnails/72.jpg)
expressiveintelligencestudio UC Santa Cruz
By designing design within broad context, I hope to offer new insights and new opportunities for mechanizing design beyond games.
I hope the treatment of interactivity (particularly in two-level design space models) prompts a reevaluation of artifact generators for domains that are not so obviously interactive. All art is interactive.
Epilogue
![Page 73: Mechanizing Exploratory Game Design (PhD Defense)](https://reader036.vdocument.in/reader036/viewer/2022062319/5589a12fd8b42ad3608b469e/html5/thumbnails/73.jpg)
expressiveintelligencestudio
UC Santa Cruz
Thanks