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Similaire à Keynote at the 2018 SIGGRAPH Conference on Motion, Interaction and Games
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Keynote at the 2018 SIGGRAPH Conference on Motion, Interaction and Games
- 1. LABORATORY FOR QUANTITATIVE EXPERIENCE DESIGNqed.cs.utah.edu Toward a Science of Game Design Rogelio E. Cardona-Rivera Assistant Professor and Director, QED Lab School of Computing, Entertainment Arts & Engineering University of Utah rogelio@cs.utah.edu @recardona
- 4. My Work: The Big Picture Developing intelligent systems
- 5. My Work: The Big Picture Developing intelligent systems which sit at the interface of a virtual world
- 6. My Work: The Big Picture Developing intelligent systems which sit at the interface of a virtual world and a person's understanding of it,
- 7. My Work: The Big Picture Developing intelligent systems which sit at the interface of a virtual world and a person's understanding of it, to enable the automated generation of compelling interactive experiences
- 12. Three Methodological Pillars •Synthesis Game Design Narratology Psychology
- 13. Three Methodological Pillars •Synthesis •Development Game Design Narratology Psychology AI
- 14. Three Methodological Pillars •Synthesis •Development •Validation Game Design Narratology Psychology AI + HCI
- 16. Game Design is a Cognitive Science
- 17. Game Design is a Cognitive Science
- 18. Game Design is a Cognitive Science {"<start>" : "<template>", "<template>" : "<object> in which players <engagement>. | <object> that involves <characteristics>. | <object> <constraints>. | <object> characterized by <relationship>.", "<object>" : … } molleindustria. http://www.gamedefinitions.com/
- 19. Game Design is a Cognitive Science –Herbert Simon The act of transforming existing courses of action into preferred ones.
- 20. Game Design is a Cognitive Science
- 21. Talk Outline Objective: The MIG Community is well- poised to pursue a science of game design • What is a Science of Game Design and why bother? • What are examples of work in this area? • What are MIG-specific opportunities?
- 22. What is a Science of Game Design and why bother?
- 23. What is a Science of Game Design… A systematically organized body of knowledge
- 24. What is a Science of Game Design… A systematically organized body of knowledge composed of observation and experiment
- 25. What is a Science of Game Design… A systematically organized body of knowledge composed of observation and experiment that encompasses the structure and behavior of games
- 26. …and why bother? • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 27. …and why bother? • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 28. The Engineering Challenge •Costly •Technically difficult •Poorly understood
- 29. Cost of Most Expensive Games 2011 2012 2013 2014 2015 2016 $124M $80M $140M$137M $105M $200M MGSV
- 30. Development Time for them 2011 2012 2013 2014 2015 2016 5 Years 3 Years 4 Years4 Years 3 Years3 Years MGSV
- 33. Authorial Combinatorics Problem •Content authoring increases exponentially with player choice Event Action
- 34. 12 writers, 3 years 200,000 dialogue lines = approx. 1M words = 1,094,170 wordsA choose-your-own-adventure (CYOA)
- 35. 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)
- 39. …and why bother? • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 41. Graphics at the Expense of Stories
- 43. 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.
- 44. Meaningless Procedural Generation No Man’s Sky can generate 1.8 × 1019 Planets
- 45. Meaningless Procedural Generation The Kaleidoscope Effect Cognitively-grounded Procedural Content Generation (Cardona-Rivera, 2017)
- 46. …and why bother? • Games are a significant engineering challenge • Advances in technology create more problems • Research should target artifact and person
- 47. 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)
- 48. Tacit Learning and Expectations
- 49. 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
- 50. Game Design Narratology Psychology AI + HCI What are examples of work in this area?
- 51. Narratively Intelligent Game AI An example agenda in the Science of Game Design
- 52. What is Narrative Intelligence? Unique human capacity to understand our environment in terms of stories (Heider and Simmel, 1944)
- 53. •Narrative framing makes interaction more compelling Why Narrative Intelligence matters
- 54. •Narrative framing makes interaction more compelling ‣ Entertainment Why Narrative Intelligence matters video games
- 55. •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education Why Narrative Intelligence matters training simulations
- 56. •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education ‣ Engagement Why Narrative Intelligence matters gamification
- 57. Why Narrative Intelligence matters •Narrative framing makes interaction more compelling ‣ Entertainment ‣ Education ‣ Engagement •Difficult to engineer ‣ AI may help ameliorate authorial burden
- 58. Interactive Narrative (IN) • Mediates actions through a narrative framing
- 59. Interactive Narrative (IN) Designer Authors with Story Director • Mediates actions through a narrative framing • Abstraction of story as trajectory of world states ‣ Narratives as plans
- 60. Narratives as Plans • Story generation as a classical planning problem ‣ : initial state ‣ : goal conditions ‣ : set of (domain) actions, predicates, and objects • Search for sequence to transform → P = hsi, g, Di D g si gsi Na Plans and planning in narrative generation: a review of plan-based approaches to the generation of story, discourse and interactivity in narratives (Young et al., 2013)
- 61. Narratives as Plans • Actions encoded as template operators ‣ Planning Domain Definition Language Na (:action pick-up :parameters (?agent ?item ?location) :precondition (and (at ?item ?location) (at ?agent ?location)) :effect (and (not (at ?item ?location)) (has ?agent ?item)))
- 62. (: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)) Narratives as Plans • Actions encoded as template operators ‣ Planning Domain Definition Language • PDDL expanded with consenting agents Na
- 63. Automated Planning • Solution to a planning problem is a planP = hsi, g, Di ⇡ = hS, B, Li
- 64. Pick-up Automated Planning • Solution to a planning problem is a plan ‣ : steps P = hsi, g, Di ⇡ = hS, B, Li S si g Pick-up Disenchant
- 65. Automated Planning • Solution to a planning problem is a plan ‣ : steps ‣ : bindings P = hsi, g, Di ⇡ = hS, B, Li S B si g Pick-up Disenchant Pick-up
- 66. Automated Planning • Solution to a planning problem is a plan ‣ : steps ‣ : bindings ‣ : causal links (e.g. ) P = hsi, g, Di ⇡ = hS, B, Li hs1, , s2i S B L si g Pick-up Disenchant Pick-up (has ARTHUR SPELLBOOK)
- 67. 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
- 68. 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
- 69. 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))
- 70. 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))
- 71. 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))
- 72. 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
- 73. 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
- 74. 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))
- 75. 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
- 77. Interactive Narrative (IN) Na Designer Authors with Story Director si g
- 78. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player
- 79. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player Player can act as afforded by the logical world state
- 80. IN Play as Game Tree Search Na si g Pick-up Disenchant Pick-up Intended Narrative Plan
- 81. 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
- 82. IN Play as Game Tree Search Na Disenchant Pick-upPick-up si g
- 83. IN Play as Game Tree Search Na Disenchant Pick-upPick-up si g Move
- 84. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Na Move Wake-up
- 85. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Move Wake-up r q p Na • Many trajectories
- 86. IN Play as Game Tree Search Disenchant Pick-upPick-up si g Na Move Wake-up r q p … … … … • Many many trajectories
- 87. 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
- 88. 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
- 89. 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
- 90. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g Player
- 91. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g PlayerMediator
- 92. Interactive Narrative (IN) Na Interacts with Designer Authors with Story Director si g PlayerMediator • Director & Mediator collaborate ‣ Accept ‣ Re-plan around ‣ Fail user actions
- 94. Why would players pick these? D Na Ps si g … … … …
- 95. Why would players pick these? D Na Ps si g … … … … • Valued as completions
- 96. Why would players pick these? D Na Ps si g … … … … • Valued as completions • If often, represents: ‣ More work for system
- 97. Why would players pick these? D Na Ps si g … … … … • Valued as completions • If often, represents: ‣ More work for system ‣ Failure of design
- 99. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence D Na Ps … …
- 100. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension D Na Ps … …
- 101. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play D Na Ps … …
- 102. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency D Na Ps … …
- 103. Automated Design Problem •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency ‣ … D Na Ps … …
- 104. •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency … …
- 105. Example Science of Game Design •Managing the player’s intent, which fluctuates due to narrative intelligence ‣ Comprehension ‣ Role-play ‣ Desire for Agency … …
- 106. 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 … …
- 107. •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 … …
- 108. 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
- 109. Modeling Story Understanding •Readers as problem solvers •Planning is 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)
- 110. Modeling Story Understanding •Readers as problem solvers •Planning is a model of problem solving •Idea: narrative plan as a proxy for mental state (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)
- 111. Understanding as Planning • 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)
- 112. Understanding as Planning 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)
- 113. Understanding as Planning 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)
- 114. Understanding as Planning 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)
- 115. Understanding as Planning 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)
- 116. Understanding as Planning 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)
- 117. Understanding as Planning 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)
- 118. Understanding as Planning Plan to QUEST Graph Mapping Algorithm D Na Ps 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 Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 119. Understanding as Planning Plan to QUEST Graph Mapping Algorithm D Na Ps 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 Mapping data structure semantics to cognitive semantics Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 120. Understanding as Planning •Replicated QUEST Validation experiment ‣ Original: manual graph ‣ Ours: generated graph • Participants gave goodness-of-answer Likert data for Q&A pairs ‣ Predicted their answers ‣ Strong support for model (N=695) Evaluating the Mapping D Na Ps (Graesser, Lang, and Roberts, 1991) Question Answering in the Context of Stories Generated by Computers (Cardona-Rivera, Price, Winer, and Young, 2016)
- 121. Understanding as Planning 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)
- 122. Understanding as Planning 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)
- 123. Understanding as Planning 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)
- 124. •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 … …
- 125. •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 … …
- 126. 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
- 127. 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
- 128. 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
- 129. 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
- 130. 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
- 131. 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
- 132. 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
- 133. 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
- 134. 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!
- 135. •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 … …
- 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. 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
- 138. 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
- 139. 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
- 140. 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
- 141. 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
- 142. 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
- 143. 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
- 144. 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
- 145. 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
- 146. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Chronology Na Ps
- 147. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps Chronology Inferences
- 148. Agency as Function of Outcomes Takeaway Foreseeing Meaningful Choices (Cardona-Rivera, Robertson, Ware, Harrison, Roberts, and Young, 2014) Na Ps Chronology ⇢ ,agency
- 149. •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 … …
- 150. 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 … …
- 151. What are examples of work in this area? • Modeling Story Comprehension as Planning • Modeling Role-play as a Preference over Actions • Modeling Agency as a Function of Choice Outcome Differences
- 154. Fidelity for Designed Purpose •How much display fidelity is enough?
- 155. Fidelity for Designed Purpose •How much display fidelity is enough? •How much display fidelity is enough for X purpose?
- 156. 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?
- 157. 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
- 158. 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
- 159. Storytelling through Motion •Movement attracts attention first
- 160. Storytelling through Motion •Movement attracts attention first
- 161. Storytelling through Motion •Movement attracts attention first •Classes of movement (Kurosawa) ‣ Nature ‣ Groups of People ‣ Individuals ‣ Camera Every Frame a Painting. - https://www.youtube.com/watch?v=doaQC-S8de8
- 162. What are MIG-specific opportunities in the Science of Game Design? • Fidelity for Designed Purpose • Understanding the Role of Inferencing & Expectations • Storytelling through Motion
- 163. Recap • What is a Science of Game Design and why bother? • What are examples of work in this area? • What are MIG-specific opportunities? Takeaway: The MIG Community is well- poised to pursue a science of game design
- 164. Call to Action • Embrace Design ‣ No optimal solutions, only tradeoffs (“it depends”) • Tripartite Model of Games Research ‣ Seek the invariant relationships Content Game Interface Cognition