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Narrative Encoding for Computational Reasoning and Adaptation UC Santa Cruz UC Santa Cruz Electronic Theses and Dissertations Title Narrative Encoding for Computational Reasoning and Adaptation Permalink https://escholarship.org/uc/item/7jc9r478 Author Harmon, Sarah Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA SANTA CRUZ NARRATIVE ENCODING FOR COMPUTATIONAL REASONING AND ADAPTATION A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER SCIENCE by Sarah M. Harmon March 2017 The Dissertation of Sarah M. Harmon is approved: Professor Arnav Jhala, Chair Professor Michael Mateas Jill Denner, PhD Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Sarah M. Harmon 2017 Contents List of Figures vii List of Tables viii Abstract ix Dedication xi Acknowledgments xii 1 Introduction 1 1.1 How Stories Transform Us . .3 1.2 Problem . .5 1.3 Contributions . .6 1.4 Outline . .8 2 Theoretical Foundations 9 2.1 Cognitive and Social Psychology . .9 2.1.1 Levels of Discourse Comprehension . .9 2.1.2 Narrative Interpretation . 12 2.2 Computational Models . 13 2.2.1 Narrative Functions . 13 2.2.2 Grammars . 13 2.2.3 Frames . 14 2.2.4 Plot Units . 15 2.2.5 Specialized Partial Models . 16 2.2.6 Formal Ontologies . 17 2.2.7 Surface-Level Adaptation . 20 2.3 Discussion . 21 iii 3 Case Study: Story Intention Graphs for Narrative Reasoning 22 3.1 Creative Story Generation . 23 3.1.1 Event Ordering . 24 3.1.2 Node Construction . 24 3.1.3 Chain Construction . 25 3.1.4 Results . 26 3.1.5 Reflection . 27 3.2 Measuring Narrative Complexity . 28 3.2.1 The Automated INC Scoring Process . 29 3.2.2 Character . 30 3.2.3 Setting . 30 3.2.4 Initiating Event . 31 3.2.5 Internal Response . 31 3.2.6 Plan . 32 3.2.7 Action/Attempt . 32 3.2.8 Complication . 33 3.2.9 Consequence . 33 3.2.10 Formulaic Markers . 34 3.2.11 Temporal Markers . 34 3.2.12 Causal Adverbial Clauses . 34 3.2.13 Knowledge of Dialogue . 35 3.2.14 Evaluation . 36 3.2.15 Reflection . 36 3.3 Discussion . 37 4 The Rensa Framework 39 4.1 Goals . 39 4.2 Framework Overview . 41 4.3 Querying Brains . 41 4.4 Bridge to Formal Logic . 42 4.5 Surface Realization . 43 4.6 Automated Visualization . 43 4.7 Summary . 44 5 SimpREL: A Simple Rensa Encoding Method 46 5.1 Encoding with Rensa . 46 5.2 Relations . 48 5.3 Basic Concepts . 51 5.4 Attributes . 53 5.4.1 Common Attributes for Actions and Conditions . 53 5.4.2 Story Attributes . 55 5.4.3 Belief Attributes . 56 5.4.4 Miscellaneous Attributes . 57 iv 5.5 The Empty Slot Variable . 57 5.6 Referencing Other Assertions . 60 5.7 Logic Support . 60 5.8 Automated Extraction Support . 61 5.9 Discussion . 70 6 Support for Existing Representations 71 6.1 Grammars . 71 6.1.1 Story Grammars . 71 6.1.2 Universal Dependencies . 73 6.1.3 DSyntS . 74 6.2 Frame-Based Models . 75 6.3 Plot Unit Representations . 76 6.3.1 Bremond's Approach . 76 6.3.2 Plot Units . 77 6.3.3 Doxastic Preference Frameworks (DPFs) . 77 6.3.4 Story Intention Graphs (SIGs) . 78 6.4 Narrative Event Chains (NECs) . 79 6.5 Social Graphs . 79 6.6 Bartalesi's Ontology and OWL . 80 6.7 ConceptNet . 81 6.8 Game-o-Matic . 82 6.9 Discussion . 83 7 Narrative Simplification 84 7.1 The Index of Narrative Complexity . 85 7.2 Narrative Simplification with Rensa . 88 7.2.1 Characters . 88 7.2.2 Complications . 89 7.3 Discussion . 90 8 Existing Applications 93 8.1 Fairy Tale Adaptation . 93 8.2 Game Generation . 95 8.3 Dilemma Generation . 96 9 Conclusion 100 9.1 Summary . 100 9.2 Future Work . 101 9.3 Closing Remarks . 104 Bibliography 106 v A Key SimpREL Examples 125 A.1 States . 125 A.2 Beliefs . 127 A.3 Goals and Intentions . 128 A.4 Imperatives . 129 B SimpREL Encodings that Demonstrate Support for Existing Repre- sentations 132 B.1 The Classic Schankian Restaurant Script . 132 B.2 DPF Analysis of Engagement ........................ 141 B.3 SIG Analysis of The Wily Lion ....................... 144 vi List of Figures 3.1 A SIG encoding derived from Amelia's Story . 25 3.2 Alternate tellings of Amelia's Story . 26 3.3 An excerpt from Deadeye Dick ....................... 27 4.1 A screenshot of Rensa's story authoring interface . 44 5.1 Example relation extraction with FRED . 63 5.2 Using Rensa to predict actor roles . 68 6.1 Example SimpREL concept mapping for a Game-o-Matic micro-rhetoric 82 7.1 Using Rensa to identify narrative complications . 87 8.1 Using Rensa to visualize game mechanics . 97 B.1 DPF game tree of Engagement ....................... 142 B.2 SIG analysis of The Wily Lion ....................... 145 vii List of Tables 2.1 A comparison of narrative model philosophies . 16 2.2 Thirteen primitive relations for temporal intervals . 19 3.1 Evaluation of the automated narrative complexity assessment system . 35 5.1 Classic SimpREL relation types . 48 5.2 Example SimpREL variable relations of the form has x .......... 49 5.3 Example SimpREL discourse relations . 51 5.4 SimpREL attributes to specify action . 54 5.5 SimpREL attributes to specify place, time, and amount . 55 5.6 A comparison of relation extraction tools . 63 5.7 Results of actor role prediction . 69 viii Abstract Narrative Encoding for Computational Reasoning and Adaptation.
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