University of California Santa Cruz Crafting Stories
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UNIVERSITY OF CALIFORNIA SANTA CRUZ CRAFTING STORIES THROUGH PLAY A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER SCIENCE by Ben Samuel December 2016 The Dissertation of Ben Samuel is approved: Professor Noah Wardrip-Fruin, Chair Professor Michael Mateas Professor Ian Horswill Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Ben Samuel 2016 Table of Contents List of Figures .................................... xv List of Tables ..................................... xvii Abstract ........................................ xviii Acknowledgments .................................. xx 1 Introduction to Shared Authorship ..................... 1 1.1 Shared Authorship Through Examples . 4 1.1.1 The Secret of Monkey Island and Interactive(?) Narrative . 4 1.1.2 Quest for Glory, Mass Effect, and Agency . 9 1.2 The Pleasures of Shared Authorship . 21 1.2.1 The Act of Creating . 23 1.2.2 The Act of Sharing . 25 1.2.3 Learning About Your Collaborator . 26 1.2.4 Feeling that the Collaborator is Getting to Know You, and Trust 29 1.2.5 Developing Transferable Skills as a Creator . 31 iii 1.2.6 Feeling that your Artistic Choices Took Craft and Design . 34 1.2.7 Feeling that you were an important part to the whole . 36 1.3 The Hope for Shared Authorship . 37 2 Towards a Theory of Shared Authorship ................. 43 2.1 Related Work: Technological . 44 2.1.1 Constructive and Sculptural Fiction, and the Story System . 44 2.1.2 Mixed Initiative Tools, PCG, Casual Creators, and Other Author- ing Environments . 55 2.1.3 Story Generators and Procedurally Generated Narrative . 73 2.1.4 Choice Poetics . 79 2.1.5 Fostering Human Collaboration . 82 2.1.6 Planners . 85 2.1.7 Drama Managers . 95 2.1.8 Player Modeling . 100 2.2 Related Work: Narrative and Theatre . 106 2.2.1 Brecht . 107 2.2.2 Peter Brook's Empty Space . 114 2.2.3 Improv . 117 2.3 Closing Thoughts and Coming Next . 138 3 The Axes of Shared Authorship ....................... 140 3.1 Source Experiences . 141 iv 3.2 The Axes of Shared Authorship . 153 3.3 Game Rankings, Axis Orthogonality, A Visualized Design Space, and Discussion . 162 4 CiF and Ensemble ................................ 173 4.1 An Introduction to Social Physics . 173 4.2 CiF . 175 4.2.1 Introduction to CiF . 176 4.2.2 Related Work to Social Physics . 180 4.2.3 Comme il Faut System Description . 182 4.2.4 Playable Experiences Using CiF . 205 4.2.5 Closing Thoughts of CiF . 209 4.3 The Ensemble Engine . 210 4.3.1 Introduction to Ensemble . 210 4.3.2 Expressive Features . 212 4.3.3 New Authoring Tool . 226 4.3.4 Some Closing Thoughts on Ensemble . 229 4.4 Social Physics and Shared Authorship . 231 4.4.1 The Simulative Authoring Layer: Collaborating with a Simulation 232 4.4.2 Social Simulation and Shared Authorship Difficulties . 236 4.5 Conclusion . 241 5 Prom Week .................................... 243 v 5.1 Introduction to Prom Week . 243 5.2 Prom Week Description . 244 5.2.1 Stories . 248 5.2.2 Social Physics . 252 5.3 Prom Week Evaluation . 256 5.3.1 Critical Reception . 256 5.3.2 Data Analysis . 258 5.4 Prom Week, Shared Authorship, and AI-Based Game Design . 268 5.4.1 Prom Week's Strengths . 271 5.4.2 Prom Week's Weaknesses . 279 5.5 Closing Thoughts on Prom Week . 289 6 Story Sampling, Gamalyzer, and Playspecs ................ 292 6.1 Story Sampling . 294 6.1.1 Work Related to Story Sampling . 297 6.1.2 Story Sampling Example: Repetition of Dialogue . 299 6.1.3 Story Sampling Example: Multiple Interpretations . 306 6.1.4 Closing Thoughts on Story Sampling . 310 6.2 Gamalyzer . 312 6.2.1 About Gamalyzer . 314 6.2.2 Applying Dissimilarity Metrics . 316 6.2.3 Gamalyzer Evaluation Experiment Design . 317 vi 6.2.4 Gamalyzer Results and Discussion . 321 6.2.5 Gamalyzer Closing Thoughts . 325 6.3 Playspecs . 327 6.3.1 Motivating Examples . 330 6.3.2 Playspecs . 332 6.3.3 Integration with Existing Games . 338 6.3.4 Closing Thoughts on Playspecs . 340 6.4 Conclusion . 342 7 Bad News ..................................... 344 7.1 Introduction . 344 7.2 Talk of the Town ............................... 344 7.3 Bad News ................................... 349 7.3.1 Work Related to Bad News . 352 7.3.2 The Game . 354 7.3.3 The Simulation . 359 7.3.4 The Player . 363 7.3.5 The Actor . 369 7.3.6 Sample Playthrough Summary . 375 7.3.7 Preliminary Results and Critical Reception . 378 7.3.8 Future Applications Inspired by Bad News . 382 7.4 Bad News and Shared Authorship . 384 vii 7.4.1 The Strengths of Bad News . 384 7.4.2 Bad News' Weaknesses . 400 7.5 Conclusion . 402 8 Ongoing Work and Conclusions ....................... 406 8.1 Introduction . 406 8.2 Work Related to Writing Buddy . 407 8.3 Writing Buddy System Description . 409 8.3.1 The Authoring Process . 410 8.3.2 Ensemble . 414 8.3.3 Playspecs . 416 8.3.4 Simple Example Interaction . 417 8.4 Closing Thoughts on The Future of Writing Buddy . 418 8.5 The End . 422 Bibliography ..................................... 427 viii List of Figures 1.1 The Bead of Strings for The Secret of Monkey Island. Any given bead represents one of the four \parts" of the game. Though there is room for variation of player performance within a bead, there exists only one set of solutions for the puzzles, and thus only one way to progress from part to part. The player's actions in any given part have little to no bearing on the others. 11 3.1 A two dimensional visualization of the original eleven dimensional space. One can see clear groupings of like games, as well as largely unexplored design space in the upper right quadrant of the map, boldly being pio- neered by Fa¸cade.. 169 ix 4.1 System architecture diagram of CiF. Characters, the current social state, the history stored in the social facts database, along with authored so- cial exchanges, microtheories, and the cultural knowledge base are used to inform CiF's procedures. Volition formation determines what social exchange characters want to do with one another. After social exchange selection, which is handled by the playable experience leveraging CiF, CiF determines if the responder will accept or reject the intent of the social exchange. The most salient instantiation is selected, and then cus- tomized with NLG templates to be consistent with the social state. After presenting the instantiation through performance realization (again, han- dled by the game using CiF), the effect changes are processed, updating the social state. Finally, trigger rules are executed, which potentially further change the social state, setting the stage for another round of volition formation. 183 4.2 Data flow of schema package components. Social world authors create a schema package. After a validation process, elements of the schema package populate the initial state of the social record, and the action and rule libraries. 213 4.3 Three major processing elements of the Ensemble Engine . 219 4.4 The Rule Viewer, showing a filterable list of the volition rules authored in the loaded schema package. Clicking a rule will open it in the rule editor.228 x 4.5 The Rule Editor, showing a dynamically constructed predicate editor for a volition rule. 229 5.1 A screenshot of one of Prom Week's opening levels. Players click on pairs of characters to see what social exchanges they would like to take towards one another. 244 5.2 A screenshot of the Prom Week interface. Oswald has been selected as the initiator, and Doug is the responder. The far left thought bubble contains all of the social exchanges Oswald wants to do with Doug, the product of volition formation reasoning over the current social state. 245 5.3 After the player selected for Oswald to \Bully" Doug, an instantiation is selected (and plays out) in which Oswald draws on past social history to make fun of Doug for an action he did in the game's backstory. 246 5.4 Doug's social goals for his campaign, encoded in the same rule system that drives character volitions. 249 xi 5.5 Play trace graph showing how often each distinct path through Simon's story was traversed (shown by the number associated with each node, emphasized with color). The large band of nodes seen at the top of the diagram represents approximately one third of the total size of the complete graph. The cutout shows.