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Salt Lake City Public Library Tech Talk - Toward a Science of Game Design
- 1. LABORATORY FOR QUANTITATIVE EXPERIENCE DESIGNqed.cs.utah.edu Toward a Science of Game Design Rogelio E. Cardona-Rivera, Ph.D. Assistant Professor and Director, QED Lab School of Computing, Entertainment Arts & Engineering University of Utah rogelio@cs.utah.edu @recardona
- 2. Games are a dominant part of everyday life
- 3. Essential Facts About the Computer & Video Game Industry (Entertainment Software Association, 2018)
- 4. 64% of American households are home to someone who plays video games Essential Facts About the Computer & Video Game Industry (Entertainment Software Association, 2018)
- 5. 64% of American households are home to someone who plays video games In 2018, the video game industry contributed $32 B to the US GDP Essential Facts About the Computer & Video Game Industry (Entertainment Software Association, 2018)
- 6. Games beyond entertainment Training simulations Gamification Science discovery Procedural rhetoric
- 7. Problem There is no systematically organized body of knowledge
- 8. Problem There is no systematically organized body of knowledge composed of observation and experiment
- 9. Problem There is no systematically organized body of knowledge composed of observation and experiment that encompasses the structure and behavior of games
- 10. A Science of Game Design A systematically organized body of knowledge composed of observation and experiment that encompasses the structure and behavior of games
- 11. Talk Outline Objective: A Science of Game Design is socially relevant, meaningful, and impactful • Why bother with a Science of Game Design? • What are examples of work in this area? • What are some open challenges?
- 12. Why bother with a Science of Game Design?
- 13. A Science of Game Design is Needed • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 14. A Science of Game Design is Needed • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 15. The Engineering Challenge •Costly •Technically difficult •Poorly understood
- 16. Cost of Most Expensive Games 2011 2012 2013 2014 2015 2016 $124M $80M $140M$137M $105M $200M MGSV
- 17. Development Time for them 2011 2012 2013 2014 2015 2016 5 Years 3 Years 4 Years4 Years 3 Years3 Years MGSV
- 22. Authorial Combinatorics Problem •Content authoring increases exponentially with player choice Event Action
- 23. 12 writers, 3 years 200,000 dialogue lines = approx. 1M words = 1,094,170 wordsA choose-your-own-adventure (CYOA)
- 24. Game Purchase Influence Factors Similarity 9% Sequel 9% Word of mouth 11% Graphics 12% Interesting Story 16% Price 21% Other 22% Essential Facts About the Computer & Video Game Industry (Entertainment Software Association, 2016)
- 28. • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person A Science of Game Design is Needed
- 30. Graphics at the Expense of Stories
- 33. There’s a lot of hacks and kludges to get things working… I’m sure you would find tons of duplication of effort, definitely. I’ve been an audio programmer on [X] different games and I’ve written [X] different audio engines.
- 35. PROCEDURAL CONTENT GENERATION • Artificial intelligence for game content creation
- 36. Meaningless Procedural Generation No Man’s Sky can generate 1.8 × 1019 Planets
- 37. Meaningless Procedural Generation The Kaleidoscope Effect Cognitively-grounded Procedural Content Generation (Cardona-Rivera, 2017)
- 38. • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person A Science of Game Design is Needed
- 39. The Player Modeling Principle The whole value of a game is in the mental model of itself it projects into the player’s mind. The Simulation Dream (Sylvester, 2013)
- 40. Tacit Learning and Expectations
- 41. What is a Science of Game Design and why bother?
- 42. What is a Science of Game Design and why bother? • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 44. What are examples of work in this area?
- 45. Game Design Narratology Psychology AI + HCI What are examples of work in this area?
- 47. Narratively Intelligent Game AI An example agenda in the Science of Game Design
- 48. What is Narrative Intelligence? Unique human capacity to understand our environment in terms of stories (Heider and Simmel, 1944)
- 49. •Narrative framing makes interaction more compelling Why Narrative Intelligence matters
- 50. •Narrative framing makes interaction more compelling ‣ Entertainment Why Narrative Intelligence matters video games
- 51. •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education Why Narrative Intelligence matters training simulations
- 52. •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education ‣ Engagement Why Narrative Intelligence matters gamification
- 53. Why Narrative Intelligence matters •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education ‣ Engagement •Difficult to engineer ‣ AI may help reduce authorial burden
- 54. Interactive Narrative (IN) • Mediates actions through a narrative framing
- 55. Interactive Narrative (IN) Designer Authors with Story Director • Mediates actions through a narrative framing
- 56. Example: A Knight’s Tale Na (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) …) )
- 57. Example: A Knight’s Tale (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)) (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) …) ) Na
- 58. Example: A Knight’s Tale (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)) Na
- 59. Example: A Knight’s Tale Na (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))
- 60. Example: A Knight’s Tale Na (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))
- 61. Example: A Knight’s Tale Na (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))
- 62. Example: A Knight’s Tale (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)) Na
- 63. Example: A Knight’s Tale (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)) Na
- 64. Example: A Knight’s Tale Na (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))
- 65. Example: A Knight’s Tale Na (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)) si g Pick-up Disenchant Pick-up
- 66. Interactive Narrative (IN) Na Designer Authors with Story Director si g Pick-up Disenchant Pick-up
- 67. Interactive Narrative (IN) Na Designer Authors with Story Director si g
- 68. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player
- 69. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player Player can act as afforded by the logical world state
- 70. IN Play as Game Tree Search Na si g Pick-up Disenchant Pick-up Intended Narrative Plan
- 71. IN Play as Game Tree Search • Chronology — Player & System take turns ‣ On System Turn: Advance Narrative Agenda ‣ On Player Turn: ??? Na Disenchant Pick-upPick-up si g
- 72. IN Play as Game Tree Search Na Disenchant Pick-upPick-up si g
- 73. IN Play as Game Tree Search Na Disenchant Pick-upPick-up si g Move
- 74. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Na Move Wake-up
- 75. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Move Wake-up r q p Na • Many trajectories
- 76. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Na Move Wake-up r q p … … … … • Many many trajectories
- 77. IN Play as Game Tree Search Na Disenchant Pick-upPick-up si g Move Wake-up r q p … … … … • Many many trajectories ‣ Not all are good
- 78. IN Play as Game Tree Search Na is unreachable Disenchant Pick-upPick-up si g Move Wake-up r q p … … … g • Many many trajectories ‣ Not all are good
- 79. IN Play as Game Tree Search Na is unreachable Disenchant Pick-upPick-up si g Move Wake-up r q p … … … g • Many many trajectories ‣ Not all are good ‣ Mediator is needed
- 80. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player
- 81. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g PlayerMediator
- 82. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g PlayerMediator • Director & Mediator collaborate ‣ Accept ‣ Re-plan around ‣ Fail player actions
- 84. Why would players pick these? D Na Ps si g … … … …
- 85. Why would players pick these? D Na Ps si g … … … … • Valued as completions
- 86. Why would players pick these? D Na Ps si g … … … … • Valued as completions • If often, represents: ‣ More work for system
- 87. Why would players pick these? D Na Ps si g … … … … • Valued as completions • If often, represents: ‣ More work for system ‣ Failure of design
- 89. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence D Na Ps … …
- 90. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension D Na Ps … …
- 91. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play D Na Ps … …
- 92. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency D Na Ps … …
- 93. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency ‣ … D Na Ps … …
- 94. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency … …
- 95. Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency … …
- 96. Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem … …
- 97. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem … …
- 98. Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016) Modeling Story Understanding •Readers as problem solvers (Gerrig and Bernardo, 1994) Na Ps
- 99. Modeling Story Understanding •Readers as problem solvers •Artificial intelligence can be a model of problem solving (Gerrig and Bernardo, 1994) (Tate, 2001) D Na Ps Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 100. Modeling Story Understanding •Readers as problem solvers •Artificial intelligence can be a model of problem solving •Idea: use AI to model readers (Gerrig and Bernardo, 1994) D Na Ps Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016) (Tate, 2001)
- 101. Modeling Story Understanding • The QUEST Model of Comprehension ‣ Comprehension as Q&A • Predicts normative answers to questions ‣ Why? How? When? What enabled? What was the consequence? (Graesser and Franklin, 1990) D Na Ps Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 102. Modeling Story Understanding The QUEST Graph D Na Ps Arthur disenchants Excalibur Excalibur disenchanted Arthur wants disenchanted Arthur wants Excalibur Consequence Outcome Reason Event State Goal Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 103. Modeling Story Understanding Example QUEST “Why?” Search D Na Ps Arthur disenchants Excalibur Excalibur disenchanted Arthur wants disenchanted Arthur wants Excalibur Consequence Outcome Reason Why did Arthur disenchant Excalibur? Event State Goal Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 104. Modeling Story Understanding Example QUEST “Why?” Search D Na Ps Arthur disenchants Excalibur Excalibur disenchanted Arthur wants disenchanted Arthur wants Excalibur Consequence Outcome Reason Why did Arthur disenchant Excalibur? Event State Goal Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 105. Modeling Story Understanding Example QUEST “Why?” Search D Na Ps Arthur disenchants Excalibur Excalibur disenchanted Arthur wants disenchanted Arthur wants Excalibur Consequence Outcome Reason Why did Arthur disenchant Excalibur? Event State Goal Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 106. Modeling Story Understanding Example QUEST “Why?” Search D Na Ps Arthur disenchants Excalibur Excalibur disenchanted Arthur wants disenchanted Arthur wants Excalibur Consequence Outcome Reason Why did Arthur disenchant Excalibur? Event State Goal Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 107. Modeling Story Understanding Example QUEST “Why?” Search D Na Ps Arthur wants disenchanted Arthur wants Excalibur Reason Why did Arthur disenchant Excalibur? Goal Candidate Answers Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 108. Modeling Story Understanding Takeaway D Na Ps si g Pick-up Disenchant Pick-up Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 109. Modeling Story Understanding Takeaway D Na Ps Generation si g Pick-up Disenchant Pick-up Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 110. Modeling Story Understanding Takeaway D Na Ps Generation si g Pick-up Disenchant Pick-up Comprehension Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 111. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem … …
- 112. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Role-play Example Science of Game Design … …
- 113. Determinants of Player Choice •Tripartite Model of Player Behavior ‣ Person ‣ Player ‣ Persona (Roles) The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 (Waskul and Lusk, 2004) Na Ps
- 114. Role-play as Preferred Actions •Roles ‣ Fighter ‣ Wizard ‣ Rogue •Participants (n=210) played 1-of-3 games ‣ Assigned Role (78) ‣ Chosen Role (91) ‣ No Explicit Role (41) The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Na Ps
- 115. Role-play as Preferred Actions • Players prefer to act as expected from assigned/chosen role • Players with no explicit role self- select and remain consistent Mimesis Effect The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Na Ps
- 116. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Chronology Na Ps
- 117. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Chronology Na Ps
- 118. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Chronology Na Ps
- 119. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Chronology Inferences Na Ps
- 120. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Na Ps Chronology Inferences
- 121. Role-play as Preferred Actions Takeaway The Mimesis Effect (Domínguez, Cardona-Rivera, Vance and Roberts, 2016) ! HONORABLE MENTION FOR BEST PAPER, CHI2016 Na Ps Chronology Preferred!
- 122. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Role-play Example Science of Game Design … …
- 123. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Desire for Agency Example Science of Game Design … …
- 124. Pursuing Greater Agency •Satisfying power to take meaningful action and see the results of our decisions & choices •What is meaningful? ‣ The effect of feedback ‣ Some choices were “greater agency” ones (Murray 1997) The Wolf Among Us Achieving the Illusion of Agency (Fendt, Harrison, Ware, Cardona-Rivera and Roberts, 2012) or v. or Na Ps
- 125. Foreseeing Meaningful Choices • Idea: Greater agency — greater difference (greater meaning) • Method: Measure choice story outcomes ‣ Formalize story content ‣ Define story content difference ‣ Compare choices through story content difference Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps
- 126. A Formalism of Story Content • The Event-Indexing Model ‣ Consumers “chunk” story information into events (Zwaan, Langston, Graesser 1995) picks uppicks up disenchants space time causal goals entities space time causal goals entities space time causal goals entities Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps
- 127. Story Content Difference •Situation Vector picks up space time causal goals entities Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps
- 128. Story Content Difference •Situation Vector Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) picks up forest time point 3 primary wants excalibur arthur, excalibur Na Ps
- 129. Story Content Difference •Situation Vector •Change Function ‣ Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) picks up forest time point 3 primary wants excalibur arthur, excalibur : SV ! [0, 5] Na Ps
- 130. Story Content Difference •Situation Vector •Change Function ‣ Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) picks up forest time point 3 primary wants excalibur arthur, excalibur : SV ! [0, 5] picks up forest time point 1 primary wants excalibur arthur, spellbook Na Ps
- 131. Story Content Difference •Situation Vector •Change Function ‣ Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) picks up forest time point 3 primary wants excalibur arthur, excalibur : SV ! [0, 5] picks up forest time point 1 primary wants excalibur arthur, spellbook = 2 Na Ps
- 132. Agency as Function of Outcomes •Participants (N=88) played custom CYOA ‣ 6 binary choices •Answered 5-point Likert prompts for agency •Page Trend Test supports our theory = 0 6= 0(Vermeulen et al. 2010) H0 : MdC0 = MdC5 = MdC3 = MdC1 = MdC2 = MdC1 HA : MdC0 < MdC5 < MdC3 < MdC1 < MdC2 < MdC1 Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps
- 133. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Chronology Na Ps
- 134. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps Chronology Inferences
- 135. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps Chronology ⇢ ,agency
- 136. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Desire for Agency Example Science of Game Design … …
- 137. Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency •In the context of the Automated Design Problem … …
- 138. What are examples of work in this area?
- 139. What are examples of work in this area? • Modeling Story Comprehension through Artificial Intelligence • Modeling Role-play as a Preference over Actions • Modeling Agency as a Function of Choice Outcome Differences
- 141. What are some open challenges?
- 142. Fidelity for Designed Purpose •How much display fidelity is enough?
- 143. Fidelity for Designed Purpose •How much display fidelity is enough? •How much display fidelity is enough for X purpose?
- 144. Fidelity for Designed Purpose •How much display fidelity is enough? •How much display fidelity is enough for X purpose? •How much Y fidelity is enough for X purpose?
- 145. Fidelity for Designed Purpose •How much display fidelity is enough? •How much display fidelity is enough for X purpose? •How much Y fidelity is enough for X purpose? ‣ A Design Space Scenario Interaction Display
- 146. Inferencing & Expectations •How do mimetic interfaces elicit expectations? ‣ Interaction ‣ Motion ‣ Games Person’s Bounding Box Player’s Bounding Box Persona’s Bounding Box v. Person’s Bounding Box Player+Persona Bounding Box
- 147. Increasing Participation in Game Design •Photography used to require significant expertise •The digital camera changed that •What is the Digital Camera of Games
- 148. What are some open challenges in the Science of Game Design?
- 149. What are some open challenges in the Science of Game Design? • Fidelity for Designed Purpose • Understanding the Role of Inferencing & Expectations • Digital Camera of Games
- 150. Recap • What is a Science of Game Design and why bother? • What are examples of work in this area? • What are some open challenges? Takeaway: A Science of Game Design is socially relevant, important, and meaningful Rogelio E. Cardona-Rivera, Ph.D. Assistant Professor and Director, QED Lab University of Utah rogelio@cs.utah.edu @recardona