Browsing by Author "R. Michael Young, Committee Chair"

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Bowyer: A Planning Tool for Bridging the Gap between Declarative and Procedural Domains
(2008-02-03) Cash, Steven Patrick; R. Michael Young, Committee Chair; Robert St. Amant, Committee Member; James C. Lester, Committee Member
Cinematic Discourse Generation
(2009-04-21) Jhala, Arnav Harish; Timothy Buie, Committee Member; Jon Doyle, Committee Member; James Lester, Committee Member; R. Michael Young, Committee Chair
Narrative is one of the fundamental ways in which humans organize information. 3D virtual environments provide a compelling new medium for creating and sharing nar- ratives. In pre-rendered virtual environments like animated movies, directors communicate complex narratives by carefully constructing them shot-by-shot. To do this, a film's director exploits the viewer's familiarity with narrative patterns and cinematic idioms to effectively convey a structured story. In real-time environments like games and training simulations, however, a system has much less control over the stories that need to be told, since often novel stories are constructed on demand and tailored to a specific session or user's needs. In many contexts, stories are built not solely by the system, but collaboratively with many users whose choices for action contribute to the construction of unanticipated narrative structure. In the past, intelligent cinematography systems have been developed to automat- ically record the actions of users within a virtual world and then to construct coherent visualizations that communicate these action sequences. While these systems generate co- herent visualizations, they do not attempt to address the careful construction of narrative discourse based on established and identifiable patterns of narrative communication. Cur- rent automated camera systems take into account local coherence of shots and transitions but do not address the rhetorical coherence of the communication across multiple shots. I describe an end-to-end camera planning system - Darshak - that constructs visual narrative discourse of a given story in a 3D virtual environment. Darshak uses a hierar- chical partial order causal link planning algorithm to generate narrative plans that contain both story and camera actions. Dramatic situation patterns commonly used by writers of fictional narratives and endorsed by narrative theorists are formalized as communicative plan operators that provide a basis for structuring the cinematic content of the story's vi- sualization. The dramatic patterns are realized through abstract communicative operators that represent operations on a viewer's beliefs about the story and its telling. Camera shots and transitions are defined in this plan-based framework as execution primitives. Repre- sentation of narrative discourse as a hierarchical plan structure enables us to utilize 1) the hierarchical nature of narrative patterns and film idioms through the hierarchy in decom- positional plan operators, and 2) explicit representation of causal motivation for selection of shots through causal links. I present an empirical evaluation of the algorithm, based on cognitive metrics, for three properties of cinematic discourse: Saliency, Coherence and Temporal Consistency.
Cognitive Models of Discourse Comprehension for Narrative Generation
(2009-07-27) Niehaus, James Michael; James Lester, Committee Member; Stephen Mitroff, Committee Member; Robert St. Amant, Committee Member; Jon Doyle, Committee Member; R. Michael Young, Committee Chair
Recent work in the area of narrative generation has sought to develop systems that automatically produce experiences for a user that are understood as stories. Much of this prior work, however, has focused on the structural aspects of narrative rather than the process of narrative comprehension undertaken by readers. Cognitive theories of narrative discourse comprehension define explicit models of a reader's mental state during reading. These cognitive models are created to test hypotheses and explain empirical results about the comprehension processes of readers. They do not often contain sufficient precision for implementation on a computer, and thus, they are not yet suitable for computational generation purposes. This dissertation employs cognitive models of narrative discourse comprehension to define an explicit computational model of a reader's comprehension process during reading, predicting aspects of narrative focus and inferencing with precision. This computational model is employed in a narrative discourse generation system to select content from an event log, creating discourses that satisfy comprehension criteria. The results of three experiments are presented and discussed, exhibiting empirical support for the computational reader model and the results of generation. This dissertation makes a number of contributions that advance the state-of-the-art in narrative discourse generation: a formal model of narrative focus, a formal model of online inferencing in narrative, a method of selecting narrative discourse content to satisfy comprehension criteria, and implementation and evaluation of these models.
A Computational Model of Narrative Generation for Surprise Arousal
(2009-07-28) Bae, Byung Chull; R. Michael Young, Committee Chair; James C. Lester, Committee Member; Brad Mehlenbacher, Committee Member; Robert Rodman, Committee Member
This dissertation describes work to develop a planning-based computational model of narrative generation designed to elicit surprise in the mind of a reader. To this end, my approach makes use of two narrative devices Ã¢â‚¬â€œ flashback and foreshadowing. While surprise plays an important role for attention focusing, learning, and creativity, little effort has been made to build a computational framework for surprise arousal in narrative. In my computational model, flashback provides a backstory to explain what causes a surprising outcome, while foreshadowing gives hints about the surprise before it occurs. In this work I focus on the arousal of surprise emotion as a cognitive response which is based on a reader's cognitive appraisal of a given situation. In this dissertation I present Prevoyant, a planning-based computational model of surprise arousal in narrative generation, and analyze the effectiveness of Prevoyant. To build a computational model of the unexpectedness in surprise, I adopt a cognitive model of surprise based on expectation failure. There are two contributions made by this dissertation. First, I present a computational framework for narrative generation designed to elicit surprise. The approach makes use of a two-tier model of narrative and draws on Structural Affect Theory, which claims that a readerÃ¢â‚¬â„¢s emotions such as surprise or suspense are closely related to narrative structure. Second, I present a methodology to evaluate surprise in narrative generation using a planning-based approach based on the cognitive model of surprise causes. The results of the experiments that I conducted show strong support that my system effectively generates a discourse structure for surprise arousal in narrative.
Narrative Planning: Balancing Plot and Character
(2004-10-21) Riedl, Mark Owen; R. Michael Young, Committee Chair; James Lester, Committee Member; Jon Doyle, Committee Member; Michael Capps, Committee Member; Brad Mehlenbacher, Committee Member
The ability to generate narrative is of importance to computer systems that wish to use story effectively for a wide range of contexts ranging from entertainment to training and education. The typical approach for incorporating narrative into a computer system is for system builders to script the narrative features at design time. A central limitation of this pre-scripting approach is its lack of flexibility -- such systems cannot adapt the story to the user's interests, preferences, or abilities. The alternative approach is for the computer systems themselves to generate narrative that is fully adapted to the user at run time. A central challenge for systems that generate their own narrative elements is to create narratives that are readily understood as such by their users. I define two properties of narrative — plot coherence and character believability — which play a role in the success of a narrative in terms of the ability of the narrative's audience to comprehend its structure. Plot coherence is the perception by the audience that the main events of a story have meaning and relevance to the outcome of the story. Character believability is the perception by the audience that the actions performed by characters are motivated by their beliefs, desires, and traits. In this dissertation, I explore the use of search-based planning as a technique for generating stories that demonstrate both strong plot coherence and strong character believability. To that end, the dissertation makes three central contributions. First, I describe an extension to search-based planning that reasons about character intentions by identifying possible character goals that explain their actions in a plan and creates plan structure that explains why those characters commit to their goals. Second, I describe how a character personality model can be incorporated into planning in a way that guides the planner to choose consistent character behavior without strictly preventing characters from acting 'out of character' when necessary. Finally, I present an open-world planning algorithm that extends the capabilities of conventional planning algorithms in order to support a process of story creation modeled after the process of dramatic authoring used by human authors. This open-world planning approach enables a story planner not only to search for a sequence of character actions to achieve a set of goals, but also to search for a possible world in which the story can effectively be set.
Proactive Mediation in Plan-Based Narrative Environments
(2005-11-02) Harris, Justin Tucker; Jon Doyle, Committee Member; James Lester, Committee Member; R. Michael Young, Committee Chair
In interactive plan-based narrative environments, user's actions must be monitored to ensure that conditions necessary for the execution of narrative plans are not compromised. In the Zocalo system, management of user actions is performed on a reactionary basis by a process called mediation. In this thesis, an extension to this approach, proactive mediation, is described, which calculates responses to user input in an anticipatory manner. A proactive mediation module accepts as input a plan describing the actions being performed by the user (generated by a plan recognition system) and identifies portions of that plan that jeopardize the causal structure of the overall narrative. Once these portions are identified, proactive mediation generates modifications to the narrative plan structure that avoid the unwanted interaction between user and story. This extension to the original mediation algorithm provides more responses to a user's actions and generates responses that are more tailored to the user's actions.