The document discusses game design and the potential role of artificial intelligence. It begins by noting that [1] game design is poorly understood and costly to develop, but that generative AI could help offset these issues. However, current AI is making the problem worse by shifting the design burden and being difficult to use due to issues like the "kaleidoscope effect." The document then proposes [2] developing a better understanding of how people design games in order to build more effective AI tools. It presents some initial work analyzing game goals and narrative structures that could form the basis for such an understanding. Overall, the document argues that [3] asking how people design games is key to determining how AI can most productively assist with and

1. LABORATORY FOR QUANTITATIVE EXPERIENCE DESIGN
qed.cs.utah.edu
Rogelio E. Cardona-Rivera, Ph.D.
School of Computing | Entertainment Arts and Engineering
University of Utah
Society for the Neuroscience of Creativity
2023 Annual Meeting, Keynote
03 24 22
rogelio@cs.utah.edu
http://rogel.io
@recardona
Computational Game Design
A Frontier and Roadmap for AI-powered Artifactual Science

2. Acknowledgements

3. We have no clue
what game design is,
how to intentionally structure it,
nor how to best support it.

4. What should you learn to design games?
The view from
https://youtu.be/zQvWMdWhFCc

5. Human Cost
Telltale Games Rockstar Games

6. Event
Action
• Content authoring increases
exponentially with player choice
‣ Imagine: Choose-your-own-adventures
• The Witcher 3: Wild Hunt (2015) had:
‣ ~450,000 lines of dialogue over 3.5 years
‣ At ~5 words per dialogue: ~2.25M words
• The Game of Thrones Saga has: ~1.77M words
A Sense of Scale
Authorial Combinatorics Problem

7. The Game Design State-of-the-Art
Poorly Understood, Costly, and Effortful

8. The Game Design State-of-the-Art
Poorly Understood, Costly, and Effortful

9. The Game Design State-of-the-Art
Poorly Understood, Costly, and Effortful
Surely generative
AI can help offset
cost and effort…
…right?
A Generative AI Frontier
https://github.com/keijiro/AICommand
• Artificial intelligence
(AI) technology
could help
• Unfortunately,
current AI is making
it worse
‣ We do not
understand it
‣ It shifts the design
burden

10. Or, artificial intelligence for creating (game) content
A Generative AI Primer
Input Output
Behavior
x f(x)
f

11. With up to 1.8 x 1019 planets
A Generative AI Failure

12. One key reason Generative AI is difficult to use
The Kaleidoscope Effect
• We can summarize the generative
space quickly
‣ It ceases to be novel
• Ground (AI) truth ≠ Perceptual truth
Cardona-Rivera, Rogelio E.; Cognitively-grounded Procedural Content Generation.
In the Workshop on What’s Next for Game AI at the 31st AAAI Conference on Artificial Intelligence. 2017.

13. Second key reason Generative AI is difficult to use
Generative AI and 2nd-Order Design
• From “making design moves” to “prompt engineering”
‣ Coaxing computers to output what is needed changes how we design
Turkle, Sherry. How Computers Change the Way We Think. Chronicle of Higher Education, vol. 50, iss. 21. January 30, 2004.

14. It does not have to be this way.
AI can help us better understand game design.

15. The value in asking whether AI can design is…
…it begs the question “how do people do it?”
• Why it begs the question
• Why building AI that designs can yield insight into how people do it
• Why the future looks promising in the use of AI for design+creativity research

16. Why it begs the question
“How do people do it?”

17. What happens when you do not ask
“How do people do it?”
• Not asking the question leads to
context-less tools

18. What happens when you do not ask
“How do people do it?”
• Not asking the question leads to
context-less tools
‣ AI does not intrinsically help with
design information or process

19. What happens when you do not ask
“How do people do it?”
• Not asking the question leads to
context-less tools
‣ AI does not intrinsically help with
design information or process
‣ AI must be situated in context

20. Game Design
So, how do people do it?

21. We have no clue
what game design is,
how to intentionally structure it,
nor how to best support it.
what is
game design

22. game design
what is ?

23. • Kuittinen and Holopainen:
‣ “…an activity similar to any other design field but that the form and the content are
specific to the game design context”
• So, what is design?
game design
what is ?
Kuittinen, Jussi, and Jussi Holopainen. "Some Notes on the Nature of Game Design." In DiGRA Conference. 2009.

24. No widely-held consensus on what design is
I seek an answer by building AI that does it
Output
f(x)
Given a desired Find to produce it
Input Behavior
x f

25. • A set of activities…
• …enacted by situated agents…
• …who manipulate information…
• …to invent an artifact.
game design
what is ?
An operational definition
(which?)
(who?)
(what kinds?)
(for what?)
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.

26. situated Function—Behavior—Structure
Or, one answer to open questions of our operational definition
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.

27. A language to talk about design activity and information
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.
Function—Behavior—Structure
• 5 (artifact) information variables:
‣ F: What it accomplishes
‣ Be: What behavior achieves F
‣ S: What structure elicits Be
‣ Bs: What behavior S actually achieves
‣ D: How we manufacture S
• 8 (creative) activities
‣ Each a function over the variables

28. A modal language to talk about situated agents and artifacts
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.
situated(ness)
• 3 worlds designers inhabit:
‣ External
‣ Interpreted (designer’s understanding)
‣ Expected (designer’s imagination)
• Each FBS variable exists in all 3 worlds
‣ Worlds are modes that link the variables

29. A modal language to talk about situated agents and artifacts
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.
situated(ness)
• 3 worlds designers inhabit:
‣ External
‣ Interpreted (designer’s understanding)
‣ Expected (designer’s imagination)
• Each FBS variable exists in all 3 worlds
‣ Worlds are modes that link the variables
‣ For example: say we’re designing a game
F
i
F
i
e
F
e
What
our game
should accomplish
What you think
our game
should accomplish
What might change
what our game
should accomplish

30. situated Function—Behavior—Structure
Or, one answer to open questions of our operational definition
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.
• FBS describes:
‣ design activities
‣ design information
• s(ituatedness) describes:
‣ situated agents (i.e. designers)
‣ artifacts

31. AI-powered
Artifactual Science
Roadmap
Cardona-Rivera, Rogelio E.; Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs. In
Proceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2020.
situated Function—Behavior—Structure
• AI offers a modeling language to label
game design…
‣ …Inputs,
‣ …Behaviors, and
‣ …Outputs
• Game design science efforts can be
pinned on the map !
!
!
"
"
!
!

32. The value in asking whether AI can design is…
…it begs the question “how do people do it?”
• Why it begs the question
• Why building AI that designs can yield insight into how people do it
• Why the future looks promising in the use of AI for design+creativity research

33. Why building AI that designs can yield
insight into how people do it
The Case of Generative AI for Storygames

34. Games that mediate action through a narrative framing
• Generative AI-powered authorship
requires knowing:
‣ How do people do it today?
‣ How can AI possibly do it?
‣ Toward what end is the authorship?
- Baseline: comprehension
✦ How do people comprehend stories?
• My work to date has targeted these
Storygames
Designer
Authors
with
Story
Director

35. Be
How do people author stories today?
Clarifying narrative design practice
• The Sequence Method
‣ Question-answering driven authorship
- Keep audiences asking “why?” and “how?”
‣ No narrative design language to account for goals
• We had to invent a formal language for
game design goals
‣ The GFI Language
Cardona-Rivera, Rogelio E.; José P. Zagal, and Michael S. Debus; GFI: A Formal Approach to Narrative Design and Game Research.
In Proceedings of the 13th International Conference on Interactive Digital Storytelling, pages 133-148, 2020. # Runner-up for Best Paper
S

39. How do you accomplish that?

40. How do you accomplish that?
Finish
Super Mario Bros.

41. How do you accomplish that?
• Ultimate Goals
Finish
Super Mario Bros.
G

42. Ultimate Goals
In-game conditions players meet to succeed at a game
Win Finish Prolong

43. How do you accomplish that?
• Ultimate Goals
Finish
Super Mario Bros.
G

44. How do you accomplish that?
• Ultimate Goals
• Imperative Goals
Finish
Super Mario Bros.
Remove
<B1>
G

45. Imperative Goals
Closer-to-gameplay conditions players meet to accomplish the Ultimate
Optimize Reach Remove Solve Synchronize
Choose Configure Create Find Obtain

46. How do you accomplish that?
• Ultimate Goals
• Imperative Goals
Finish
Super Mario Bros.
Remove
<B1>
G

47. How do you accomplish that?
• Ultimate Goals
• Imperative Goals
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
G

48. How do you accomplish that?
The Ludological Goal Hierarchy
• Ultimate Goals
• Imperative Goals
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
G

49. What does that mean?
The Ludological Goal Hierarchy
• Ultimate Goals
• Imperative Goals
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
G

50. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G
Narrative Goal

51. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G
Narrative Goal
Interpretation of
Ludological Goal
I

52. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G
Narrative Goal
Interpretation of
Ludological Goal
I
How did we get there?

53. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G
Narrative Goal
Interpretation of
Ludological Goal
I
How did we get there?
Feedback
F

54. Feedback
Multi-modal stimuli intended to convey perceptual information
Phonological Lexical Grammatical Denotational

55. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G I
F
What does that mean?

56. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Remove
<B1>
Reach
<L1>
G I
F
What does that mean?

57. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Reach
<L1>
G I
F
What does that mean?

58. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Reach
<L1>
G I
F
What does that mean?

59. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
What does that mean?

60. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
What does that mean?

61. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
Why do you want to do this?

62. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
Why do you want to do this?

63. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
Why do you want to do this?

64. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
The Parallel Goal Hierarchies

65. What does that mean?
The Ludological Goal Hierarchy
Finish
Super Mario Bros.
Remove
<B1>
Reach
<L1>
Save the Princess
Defeat Bowser
Destroy Bridge
G I
F
The Parallel Goal Hierarchies
Ludological Narrative

66. Be
How do people author stories today?
Clarifying narrative design practice
• The Sequence Method
‣ Question-answering driven authorship
- Keep audiences asking “why?” and “how?”
‣ No narrative design language to account for goals
• We had to invent a formal language for
game design goals
‣ The GFI Language
Cardona-Rivera, Rogelio E.; José P. Zagal, and Michael S. Debus; GFI: A Formal Approach to Narrative Design and Game Research.
In Proceedings of the 13th International Conference on Interactive Digital Storytelling, pages 133-148, 2020. # Runner-up for Best Paper
S

67. Be
How do people author stories today?
Clarifying narrative design practice
• The Sequence Method
‣ Question-answering driven authorship
- Keep audiences asking “why?” and “how?”
‣ No narrative design language to account for goals
• We had to invent a formal language for
game design goals
‣ The GFI Language
✴ AI-driven Inquiry makes design practice precise
Cardona-Rivera, Rogelio E.; José P. Zagal, and Michael S. Debus; GFI: A Formal Approach to Narrative Design and Game Research.
In Proceedings of the 13th International Conference on Interactive Digital Storytelling, pages 133-148, 2020. # Runner-up for Best Paper
S

68. Games that mediate action through a narrative framing
• Generative AI-powered authorship
requires knowing:
‣ How do people do it today?
‣ How can AI possibly do it?
‣ Toward what end is the authorship?
- Baseline: comprehension
✦ How do people comprehend stories?
• My work to date has targeted these
Storygames
Designer
Authors
with
Story
Director

69. • Event-based representation: narratives as plans
‣ Story generation as a classical planning problem
- : initial state
- : goal conditions
- : set of (domain) actions, relations, and objects
‣ Given , find the sequence of actions to transform ⟶
How might AI author stories?
Abstracting story structures as data structures
S
P = hsi, g, Di
D
g
si
g
si
P = hsi, g, Di
Young, R. Michael, et al. "Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and
interactivity in narratives." Sprache und Datenverarbeitung, Special Issue on Formal and Computational Models of Narrative 37.1-2 (2013): 41-64.

70. Narratives as Plans
• Actions encoded as template operators (events)
‣ Planning Domain Definition Language
• PDDL expanded with consenting agents
‣
(:action pick-up
:parameters (?agent ?item ?location)
:precondition (and (at ?item ?location) (at ?agent ?location))
:effect (and (not (at ?item ?location))
(has ?agent ?item))
:agents (?agent))

71. Automated Planning
AI paradigm that reasons about action and change
• Solution to planning problem : a plan
‣ : steps
‣ : variable bindings
‣ : causal links
- e.g.
P = hsi, g, Di ⇡ = hS, B, Li
S
hs1, , s2i
B
L
si g
Pick-up
Disenchant
Pick-up
(has ARTHUR SPELLBOOK)

72. A Knight’s Tale
Example AI-generated Storygame
(define (domain KNIGHT)
(:requirements :strips)
(:predicates
(at ?x ?y) (has ?x ?y) (path ?x ?y)
(asleep ?x) (enchanted ?x))
(:action pick-up
:parameters (?agent ?item ?location) …)
(:action move
:parameters (?agent ?from ?to) …)
(:action disenchant
:parameters (?agent ?obj ?location ?book) …)
(:action wake-up
:parameters (?agent ?sleeper ?location) …)
(define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

73. A Knight’s Tale
Example AI-generated Storygame (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

74. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

75. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

76. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

77. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

78. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

79. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

80. (define (problem STORY)
(:domain KNIGHT)
(:objects
ARTHUR MERLIN
SPELLBOOK MERLINBOOK
EXCALIBUR FOREST HOME)
(:init
(at ARTHUR FOREST)
(at MERLIN FOREST)
(has MERLIN MERLINBOOK)
(asleep MERLIN)
(at SPELLBOOK FOREST)
(at EXCALIBUR FOREST)
(enchanted EXCALIBUR)
(path FOREST HOME))
(:goal
(has ARTHUR EXCALIBUR))
A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015

81. A Knight’s Tale
Example AI-generated Storygame
Rogelio E. Cardona-Rivera and R. Michael Young; Symbolic Plan Recognition in Interactive Narrative Environments.
Joint Workshop on Intelligent Narrative Technologies and Social Believability in Games (INT 8), pages 16-22, Santa Cruz, CA, USA, 2015
si g
Pick-up
Disenchant
Pick-up
The Player Script

82. • Event-based representation: narratives as plans
‣ Story generation as a classical planning problem
- : initial state
- : goal conditions
- : set of (domain) actions, relations, and objects
‣ Given , find the sequence of actions to transform ⟶
✴ AI-driven Inquiry affords evaluating necessity and sufficiency criteria
How might AI author stories?
Abstracting story structures as data structures
S
P = hsi, g, Di
D
g
si
g
si
P = hsi, g, Di
Young, R. Michael, et al. "Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and
interactivity in narratives." Sprache und Datenverarbeitung, Special Issue on Formal and Computational Models of Narrative 37.1-2 (2013): 41-64.

83. Games that mediate action through a narrative framing
• Generative AI-powered authorship
requires knowing:
‣ How do people do it today?
‣ How can AI possibly do it?
‣ Toward what end is the authorship?
- Baseline: comprehension
✦ How do people comprehend stories?
• My work to date has targeted these
Storygames
Designer
Authors
with
Story
Director

84. Toward what end is authorship?
Baseline: that people comprehend AI-generated storygames
So, how do people understand stories?
S
Bs

85. Modeling Story(game) Understanding
A Computational-Cognitive Model
• Readers as Problem Solvers
• Automated Planning is a model of
Problem Solving
• Idea:
Use Narrative Plan as proxy for Mental State
Gerrig, Richard J., and Allan BI Bernardo. "Readers as problem-solvers in the experience of suspense." Poetics 22.6 (1994): 459-472.

86. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur
disenchants
Excalibur
Excalibur
disenchanted
Arthur wants
disenchanted
Arthur wants
Excalibur
Consequence
Outcome
Reason
Event
State
Goal

87. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur
disenchants
Excalibur
Excalibur
disenchanted
Arthur wants
disenchanted
Arthur wants
Excalibur
Consequence
Outcome
Reason
Event
State
Goal
Why did Arthur
disenchant Excalibur?

88. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur
disenchants
Excalibur
Excalibur
disenchanted
Arthur wants
disenchanted
Arthur wants
Excalibur
Consequence
Outcome
Reason
Event
State
Goal
Why did Arthur
disenchant Excalibur?

89. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur
disenchants
Excalibur
Excalibur
disenchanted
Arthur wants
disenchanted
Arthur wants
Excalibur
Consequence
Outcome
Reason
Event
State
Goal
Why did Arthur
disenchant Excalibur?

90. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur
disenchants
Excalibur
Excalibur
disenchanted
Arthur wants
disenchanted
Arthur wants
Excalibur
Consequence
Outcome
Reason
Event
State
Goal
Why did Arthur
disenchant Excalibur?

91. Understanding as Planning
• The QUEST Model
(Graesser and Franklin, 1990)
‣ Comprehension
operationalized as Q&A
‣ Predicts normative answers
to Qs about events
- Why | How | When did
event X happen? What
enabled | What was the
consequence of event X?
‣ Predictions generated from
The QUEST Graph
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Arthur wants
disenchanted
Arthur wants
Excalibur
Reason
Goal
Why did Arthur
disenchant Excalibur?
Candidate Answers

92. Understanding as Planning
Narrative Plan to QUEST Graph Mapping Algorithm
Given a plan :
1. , generate event node ei with
a. effects , generate state node ti with
2. Connect Consequence Arcs for all ti → ei , ei →ti+1 in
3. For all literals in , generate goal node li with
4. Connect Reason Arcs for all goal nodes, by ancestry
5. Connect Outcome Arcs for all li →ei in
B
L
⇡ = hS, B, Li
B
L
L B
8s 2 S
8 e 2 S
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.
Mapping
Data Structure
Semantics
to
Cognitive
Semantics

93. Understanding as Planning
Evaluating the Mapping
• Replicated the QUEST Validation Experiment
(Graesser, Lang, and Roberts, 1991)
‣ Original: manually-created QUEST graph
‣ Ours: generated QUEST graph
• Participants gave goodness-of-answer
Likert ratings for Q&A pairs
‣ We used our generated QUEST graph to predict ratings
‣ Strong support for our model (N=695)
Cardona-Rivera, R. E., et al.; Question Answering in the Context of Stories Generated by Computers.
Advances in Cognitive Systems, 4, pages 227-246, 2016.

94. Toward what end is authorship?
Baseline: that people comprehend AI-generated storygames
So, how do people understand stories?
✴ AI-driven Inquiry affords imputing computational
mechanisms to design-relevant cognition
S
Bs

95. Games that mediate action through a narrative framing
• Generative AI-powered authorship
requires knowing:
‣ How do people do it today?
‣ How can AI possibly do it?
‣ Toward what end is the authorship?
- Baseline: comprehension
✦ How do people comprehend stories?
• My work to date has targeted these
Storygames
Designer
Authors
with
Story
Director

96. Why building AI that designs can yield
insight into how people do it
The Case of Generative AI for Storygames

97. Why building AI that designs can yield
insight into how people do it
The Case of Generative AI for Storygames
Generation
si g
Pick-up
Disenchant
Pick-up
Comprehension
Authorship

98. The value in asking whether AI can design is…
…it begs the question “how do people do it?”
• Why it begs the question
• Why building AI that designs can yield insight into how people do it
• Why the future looks promising in the use of AI for design+creativity research

99. Why the future looks promising in using AI
for design+creativity research
The Search for Invariants

100. On Invariants
…in Natural Phenomena …in Artifactual Phenomena
• Predictive relationship that
relates quantities of interest
• Predictive relationship that
relates quantities of interest
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F = m ⇥ a
<latexit sha1_base64="Rw4UFwukpYQGXy0YZJLiUajRZYc=">AAACDHicbVDLSgNBEJyNrxhfUY9eBoPgKeyKohchkIuejGIekKxhdjKbDJmdWWZ6hbDkFzx71W/wJl79Bz/Bv3A2yUETCxqKqm66u4JYcAOu++XklpZXVtfy64WNza3tneLuXsOoRFNWp0oo3QqIYYJLVgcOgrVizUgUCNYMhtXMbz4ybbiS9zCKmR+RvuQhpwSs9HCDL/E17gCPmMHVbrHklt0J8CLxZqSEZqh1i9+dnqJJxCRQQYxpe24Mfko0cCrYuNBJDIsJHZI+a1sqiV3jp5Orx/jIKj0cKm1LAp6ovydSEhkzigLbGREYmHkvE//1MgWUEmbuAAgv/JTLOAEm6XR/mAgMCmfJ4B7XjIIYWUKo5vYFTAdEEwo2v4LNxptPYpE0TsreWdm9PS1V7mYp5dEBOkTHyEPnqIKuUA3VEUUaPaMX9Oo8OW/Ou/Mxbc05s5l99AfO5w81G5sM</latexit>
O = I ⇥ C

101. On Invariants
<latexit sha1_base64="Rw4UFwukpYQGXy0YZJLiUajRZYc=">AAACDHicbVDLSgNBEJyNrxhfUY9eBoPgKeyKohchkIuejGIekKxhdjKbDJmdWWZ6hbDkFzx71W/wJl79Bz/Bv3A2yUETCxqKqm66u4JYcAOu++XklpZXVtfy64WNza3tneLuXsOoRFNWp0oo3QqIYYJLVgcOgrVizUgUCNYMhtXMbz4ybbiS9zCKmR+RvuQhpwSs9HCDL/E17gCPmMHVbrHklt0J8CLxZqSEZqh1i9+dnqJJxCRQQYxpe24Mfko0cCrYuNBJDIsJHZI+a1sqiV3jp5Orx/jIKj0cKm1LAp6ovydSEhkzigLbGREYmHkvE//1MgWUEmbuAAgv/JTLOAEm6XR/mAgMCmfJ4B7XjIIYWUKo5vYFTAdEEwo2v4LNxptPYpE0TsreWdm9PS1V7mYp5dEBOkTHyEPnqIKuUA3VEUUaPaMX9Oo8OW/Ou/Mxbc05s5l99AfO5w81G5sM</latexit>
O = I ⇥ C
Outer
environment
(People)
Inner
environment
(Artifact)
Context
…in Artifactual Phenomena
• Predictive relationship that
relates quantities of interest
‣ Design Science Equations
‣ Equations as AI systems
• Why I am excited for the future: further precision demands your participation
• Why I am excited for the future: further precision demands your participation

102. However…

103. AI stands poised
to help you too
via
systematically organized
neurocognitive
computation
Westlin, Christiana, et al. "Improving the study of brain-behavior relationships by revisiting basic assumptions." Trends in cognitive sciences (2023).

104. LABORATORY FOR QUANTITATIVE EXPERIENCE DESIGN
qed.cs.utah.edu
• Why it begs the question
‣ If we don’t, we get context-less tools that muddle design
• Why building AI that designs can yield insight into how people do it
‣ The Case of Generative AI for Storygames
• Why the future looks promising in the use of AI for design+creativity research
‣ Reciprocal relationship between AI, Design, and Neuroscience
…it begs the question “how do people do it?”
rogelio@cs.utah.edu
http://rogel.io
@recardona
The value in asking whether AI can design is…