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Il Ne Voyageait Pas, Il Décrivait Une Circonférence. C'était Un Il ne voyageait pas, il décrivait une circonférence. C’était un corps grave, parcourant une orbite autour du globe terrestre, suivant les lois de la méchanique rationelle. – Jules Verne The mind is its own place and in itself, can make a Heaven of Hell, a Hell of Heaven. – John Milton University of Alberta Automated Planning and Player Modelling for Interactive Storytelling by Alejandro Ramirez Sanabria A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science Department of Computing Science c Alejandro Ramirez Sanabria Fall 2013 Edmonton, Alberta Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author’s prior written permission. To Fran, my family, and my closest friends: words have not yet been crafted to express the feelings of admiration and gratitude I hold dear in my heart for you. Abstract Interactive Storytelling (IS) acknowledges that people want to be participants in the unfolding of a story plot. Given the complex nature of IS, Artificial Intelligence (AI) methods can be called upon to improve and enhance interactive stories in video games. In the past decade, a number of experience management systems have been proposed but only a few have been evaluated; furthermore, none of the evaluated systems has offered both player modelling and a story generation module. In the present work, I propose PAST (Player-specific Automated Storytelling), a new expe- rience management system that welds together two complementary approaches for increasing player agency: generative experience management and player modelling. I present a set of empirical evaluations involving 320 human participants in which my system is shown to have a positive effect on perceived player agency. Acknowledgements I would like to recognize the effort that several faculty members and students at the University of Alberta have put into the realization of this work: my supervisor Vadim Bulitko for his insightful comments, support and direction of this research; Marcia Spetch for her advice and serving on my thesis committee; Wayne DeFehr for helping us co-author the Little Red Riding Hood story; Michael Bowling for serving on my thesis committee; J. Nelson Amaral for his mentoring; David Thue for his insightful comments and suggestions; and the IRCL members who so eagerly provided feedback and participation whenever prompted. I also appreciate the valuable contributions of people unaffiliated with the Uni- versity who made this work possible: Mark Riedl for collaborating on the code and consultation; Shane Riczu for helping sketch out with the first version of the Little Red Riding Hood; Matthew Brown for his help with Lisp and SBCL; and Kelly Arbeau for her assistance with the participant research pool. Finally, I want to mention my family and friends for their support, encourage- ment and love: thanks for always being there and for lending a hand when I needed it the most. Contents 1 Introduction 1 1.1 Motivation . .2 1.1.1 Entertainment Industry . .2 1.1.2 Automation . .4 1.1.3 Adapting to Player Preferences . .5 1.2 Interactive Storytelling in Computer Systems . .6 1.2.1 Drama and Experience Management . .6 1.2.2 Generative Experience Management . .9 2 Problem Formulation 11 2.1 Experience Management as a Formal Problem . 11 2.1.1 The Problem from the Authorial Perspective . 13 2.1.2 The Problem from the Experience Manager’s Perspective . 13 3 Related Work 15 3.1 Interactive Storytelling (IS), Drama and Experience Management . 15 3.2 Agency . 17 3.3 Automation and Emergent Narrative . 17 3.4 Evaluation . 18 4 Proposed Approach 20 4.1 Introduction to PAST . 20 4.1.1 Player-specific Automated Storytelling (PAST) High-level Model 21 4.2 The MDP in PAST . 22 4.2.1 Defining States . 23 4.2.2 Defining Actions . 23 4.3 Planning and Narrative Generation in PAST . 25 4.3.1 Narrative Representation . 25 4.3.2 Planning . 26 4.4 Transition Function Adjustment . 28 4.5 Player Model . 30 4.6 Interface . 33 5 Evaluation 34 5.1 Experimental Design . 34 5.1.1 Methodology . 34 5.1.2 Participants . 35 5.1.3 Experimental Setup . 35 5.1.4 Post-experience Survey Instrument . 35 5.1.5 Statistical Analysis . 35 5.1.6 Outlier Removal . 37 5.2 User Study 1: Evaluating Planning-Based Experience Management . 37 5.2.1 Story 1 . 37 5.2.2 Participants . 38 5.2.3 Results . 38 5.3 User Study 2-A: Evaluating Planning-Based Experience Management with Player Modelling . 39 5.3.1 Story 2 . 39 5.3.2 Participants . 40 5.3.3 Results . 41 5.4 User Study 2-B: Evaluating Planning-Based Experience Management with Player Modelling . 42 5.4.1 Participants . 42 5.4.2 Results . 43 5.5 User Study 3: Evaluating PAST in a Constrained Domain . 44 5.5.1 Story 3 . 45 5.5.2 Participants . 45 5.5.3 Results . 45 6 Discussion 47 6.1 User Study 1: Evaluating Planning-Based Experience Management . 47 6.2 User Study 2: Evaluating Planning-Based Experience Management with Player Modelling . 47 6.3 User Study 3: Evaluating Planning-Based Experience Management with Player Modelling in a Constrained Domain . 48 6.4 The Pull-Back Effect . 48 6.5 Problems Encountered . 49 6.6 Applicability and Future Work . 50 7 Conclusion 52 Bibliography 53 A User Study Materials 56 A.1 Briefing and Consent Form . 57 A.2 Debriefing Form (User Study 1) . 58 A.3 Debriefing Form (User Study 2 and 3) . 59 A.4 Survey . 60 A.5 Ethics Approval . 67 A.6 Alternate Assignment . 83 B Story Transcript 87 B.1 Prologue . 87 B.1.1 Introduction . 87 B.1.2 Event 1: Fight and confront her friends if necessary to do things her way. 88 B.1.3 Event 1.A: Little Red decides to attack Billy. 88 B.1.4 Event 2: Renounce to brute force and use only her cunning. 89 B.1.5 Event 2.A: Red continues to keep the game under her com- mand by deceiving everyone. 89 B.1.6 Event 3: Daydream about quests in magical worlds with big rewards. 90 B.1.7 Event 3.A: Little Red agrees to play but she keeps on day- dreaming about her quests and treasures. 90 B.1.8 Event 4: Assume her identity and inspire her friends to follow her. 91 B.1.9 Event 4.A: Little Red continues leading her friends and gives them a reprimand for not following. 91 B.1.10 Event 5: Charm and amaze her friends with old folk stories. 92 B.1.11 Event 5.A: Little Red continues telling the story. 93 B.2 Main Quest . 93 B.2.1 Event M.1: Red leaves home. 93 B.2.2 Event M.2: Wolf sees Red and decides to follow her. 94 B.2.3 Event M.3 : Red tells Henry the Wolf about her quest to Grandma’s cottage. 95 B.2.4 Event M.4: Henry the Wolf sets up a trap for Little Red outside the cottage. 95 B.2.5 Event M.5: Henry the Wolf breaks into the cottage while Grandma sleeps. 96 B.2.6 Event M.6: Garry the Huntsman finds Wolf/Grendel in his sleep. 97 B.2.7 Event M.7: The Hunter rescues Red from Grendel/Wolf’s belly. 98 B.2.8 Event M.8: Grandma goes berserk. 98 B.2.9 Event M.9: Little Red gives Grandma a Basket full of goodies. 99 B.3 Ruptures . 99 B.3.1 Story type 1 . 99 B.3.2 Story type 2 . 102 B.4 Repairs . 105 B.4.1 Story type 1 . 105 B.4.2 Story type 2 . 106 List of Tables 1.1 Attributes of Interactive Storytelling (IS) systems. .8 4.1 The RPG player types and description used by PaSSAGE and PAST, based on types proposed by Laws [11]. 30 5.1 ANOVA results for fun in user study 1: EM is condition (control, experimental) and GS is gaming skills (gamer, non-gamer). 38 5.2 ANOVA results for agency in user study 1: EM is condition (control, experimental) and GS is gaming skills (gamer, non-gamer). 38 5.3 ANOVA results for fun in user study 2 part A: EM is condition (con- trol, experimental, exemplar) and GS is gaming skills (gamer, non- gamer). 41 5.4 ANOVA results for agency in user study 2 part A: EM is condition (control, experimental, exemplar) and GS is gaming skills (gamer, non-gamer). 41 5.5 ANOVA results for fun in user study 2 part B: EM is condition (con- trol, experimental, exemplar) and GS is gaming skills (gamer, non- gamer). 43 5.6 ANOVA results for agency in user study 2 part B: EM is condition (control, experimental, exemplar) and GS is gaming skills (gamer, non-gamer). 43 5.7 ANOVA results for fun in user study 3: EM is condition (control, experimental) and GS is gaming skills (gamer, non-gamer).
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