Browsing by Author "Jon Doyle, Committee Member"

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Affine Lie Algebras, Vertex Operator Algebras and Combinatorial Identities
(2005-03-24) Cook, William Jeffrey; Haisheng Li, Committee Co-Chair; Bojko Bakalov, Committee Member; Jon Doyle, Committee Member; Kailash C. Misra, Committee Chair
Affine Lie algebra representations have many connections with different areas of mathematics and physics. One such connection in mathematics is with number theory and in particular combinatorial identities. In this thesis, we study affine Lie algebra representation theory and obtain new families of combinatorial identities of Rogers-Ramanujan type. It is well known that when $ ilde[g]$ is an untwisted affine Lie algebra and $k$ is a positive integer, the integrable highest weight $ ilde[g]$-module $L(k Lambda_0)$ has the structure of a vertex operator algebra. Using this structure, we will obtain recurrence relations for the characters of all integrable highest-weight modules of $ ilde[g]$. In the case when $ ilde[g]$ is of (ADE)-type and k=1, we solve the recurrence relations and obtain the full characters of the adjoint module $L(Lambda_0)$. Then, taking the principal specialization, we obtain new families of multisum identities of Rogers-Ramanujan type.
Automated Scaffolding of Task-Based Learning in Non-Linear Game Environments.
(2011-01-05) Thomas, James; Robert Young, Committee Chair; Patrick Fitzgerald, Committee Member; Robert St. Amant, Committee Member; Jon Doyle, 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.
Computational Biology of Ras Proteins
(2008-04-07) Dellinger, Andrew Everette; William R. Atchley, Committee Chair; Carla Mattos, Committee Member; Jeffrey Thorne, Committee Member; Jon Doyle, Committee Member
In this research, computational biology is used to elucidate how evolutionary history has changed roles of structure and function among Ras proteins, with a focus on the Ras family. This dissertation begins with phylogenetic analyses of the Ras superfamily and Ras family. Phylogenetic trees of the Ras family were estimated using Neighbor-Joining, Weighted Neighbor-joining, Parsimony, Quartet Puzzling, Maximum Likelihood and Bayesian methods. In nearly all cases, each clade represented a subfamily. Clade members and clade divisions were consistent among all the trees, increasing the probability of a correct estimation of the evolutionary history. Further investigation into the evolution of sequence involved decomposing sequence covariation into its respective components. The roles of the functional and structural components of covariation were the focus of several multivariate analyses. Decision tree analysis, a data mining method, found that sequence divergence in critical sites of the hydrophobic core, dimerization regions and ligand binding regions were sufficient to divide Ras subfamilies. Alignments of GDP-bound and GTP-bound crystal structures revealed that only Ral and M-Ras proteins have structural variation in the effector binding switch I regions, while all Ras structures vary in the protein binding switch II region. Di-Ras2-GDP was shown to have a unique C-terminal loop which binds to the interswitch region. Last, a common factor analysis was computed. The factors contain the set of sites that both discriminate among the subfamilies and have a unique functional or structural role, such as Ral tree-determinant sites. Finally, sequence signatures were developed for each of the families of the Ras superfamily using Boltzmann-Shannon entropy. This method was compared to the PROSITE signature, profile hidden Markov model and MEME position-specific scoring matrix methods. The Entropy method identified approximately 8% fewer proteins than the best of the other methods, MEME. Comparative analyses of these sequence signatures determined which sites and amino acids played important roles in the changes in protein function and structure among Ras families.
Effective Tool Use in a Habile Agent
(2005-04-28) Wood, Alexander Burchi; Robert St. Amant, Committee Chair; R. Michael Young, Committee Member; Jon Doyle, Committee Member
Tool use is a hallmark of human intelligence, which has not fully been explored in the artificial intelligence research community. Research in cognitive neuroscience on primates suggests that not only do we maintain a mental representation of our body, but the body schema is modified to include a tool during intentional tool use (Iriki et al., 1996). We have developed a habile (tool-using) agent, based on the Sony Aibo platform, that can pick up a stick and use it as a tool to reach objects previously out of its range. The agent uses a recurrent neural network developed by Steinkühler and Cruse (1998) for maintaining an internal body schema used to find appropriate postures for reaching and grasping tools. We argue that analysis of activities of such tool using agents offers an informative way to evaluate intelligence.
Efficient Algorithms for Querying Large-Scale Data in Relational, XML, and Graph-Structured Data Repositories
(2008-08-18) Gou, Gang; Xiaohui Gu, Committee Member; Ting Yu, Committee Member; Jon Doyle, Committee Member; Rada Chirkova, Committee Chair
We live in an information age, and data are ubiquitous today. Various applications, ranging from scientific computing, medical research, and bioinformatics to administrative management, commercial sales, and financial marketing, generate and utilize data every day. Many of these applications are data intensive, with the amount of data involved potentially reaching hundreds of thousands of gigabytes. Further, different applications store data using different data models. For example, applications could store and manage structured data using a flat (relational) model, semi-structured data using a hierarchical (XML) model, and less-structured data using a more general and flexible graph model. In this thesis, I report my research results on efficiently querying large-scale data in relational, XML, and graph-structured data repositories. Specifically, this thesis covers three research projects, which I have been invited to present in the ACM SIGMOD conference in 2006, 2007, and 2008, respectively. The first project concerns efficient querying of relational data using materialized views and introduces our efficient view-based query-optimization algorithms that support a large and practically important subset of SQL queries. The second project focuses on efficiently querying XML data and presents efficient algorithms for evaluating XPath queries over XML streams, which are the first ones that achieve the O(|D||Q|) time performance, where |D| is the XML data size and |Q| is the XPath query size. Meanwhile, our algorithm EQ also achieves optimal space performance. The third project addresses efficient querying of graph-structured data, by introducing efficient algorithms for retrieving top-ranked tree-pattern matches from large graphs. While a tree-pattern query could have an extremely large, potentially exponential, number of answer matches in a graph, our algorithms exhibit time and space performance that is linear or sub-linear in the size of the input data. Our algorithms are the first ones that have this excellent performance property.
Flexible Decision-Making in Sequential Auctions
(2005-07-31) Cai, Gangshu; Peter R. Wurman, Committee Chair; Xiuli Chao, Committee Member; Jon Doyle, Committee Member; Salah E. Elmaghraby, Committee Member
Because sequential auctions have permeated society more than ever, it is desirable for participants to have the optimal strategies beforehand. However, finding closed-form solutions to various sequential auction games is challenging. Current literature provides some answers for specific cases but not for general cases. A decision support system that can automate optimal bids for players in different sequential auction games will be useful in solving these complex economic problems, which requires not only economic but also computational efficiency. This thesis contributes in several directions. First, this dissertation derives results related to the multiplicity of equilibria in first-price, sealed-bid (FPSB) auctions, and sequential FPSB auctions, with discrete bids under complete information. It also provides theoretical results for FPSB auctions with discrete bids under incomplete information. These results are applicable to both two-person and multi-person cases. Second, this thesis develops a technique to compute strategies in sequential auctions. It applies Monte Carlo simulation to approximate perfect Bayesian equilibrium for sequential auctions with discrete bids and incomplete information. It also utilizes the leveraged substructure of the game tree which can dramatically reduce the memory and computation time required to solve the game. This approach is applicable to sequences of a wide variety of auctions. Finally, this thesis analyzes the impact of information in sequential auctions with continuous bids and incomplete information when bids are revealed. It provides theoretical results especially the non-existence of pure-strategy symmetric equilibrium in both the symmetric sequential FPSB and the symmetric sequential Vickrey auctions.
Hamilton Cycle Heuristics in Hard Graphs
(2004-03-23) Shields, Ian Beaumont; Jon Doyle, Committee Member; Matthias F. Stallmann, Committee Member; Robert E. Hartwig, Committee Member; Carla D. Savage, Committee Chair
In this thesis, we use computer methods to investigate Hamilton cycles and paths in several families of graphs where general results are incomplete, including Kneser graphs, cubic Cayley graphs and the middle two levels graph. We describe a novel heuristic which has proven useful in finding Hamilton cycles in these families and compare its performance to that of other algorithms and heuristics. We describe methods for handling very large graphs on personal computers. We also explore issues in reducing the possible number of generating sets for cubic Cayley graphs generated by three involutions.
Integrating Preference Elicitation into Visualizations
(2007-05-29) Dennis, Brent Moorman; Jon Doyle, Committee Member; R. Michael Young, Committee Member; Christopher Healey, Committee Chair; Carla Savage, Committee Member
Modern technology has enabled researchers to collect large amounts of information in an expanding scope of research fields. At the same time, these new datasets are becoming more complex as evidenced in their increasing size and dimensionality. Managing and understanding these datasets has become a challenging problem. Visualizations attempt to address these concerns by creating meaningful graphical representations of data that can rapidly and accurately convey important information and interesting properties about the data to a researcher. However, many existing visualization algorithms are overwhelmed by the size of today's datasets. As a result, information is often forced off-screen due to a lack of visual resources. In previous work, we developed a navigation assistant to aid users with finding interesting data elements located off-screen. The assistant used a graph framework to provide way-finding cues and generate informative animated tours of the visualization. In order to identify which elements to include in this framework, the navigation assistant needs to model users' interests; i.e., their preferences. The efficient collection and modeling of a user's preference information is a fundamental goal of preference elicitation. Many of these techniques have yet to be applied to real-world practical problems. We address the challenges of integrating a preference model and corresponding elicitation techniques into an environment not especially suited for collecting preference information, specifically, a visualization environment. Using combinations of explicit and implicit techniques, the navigation assistant collects preference information from users both before and during their interaction with a visualization. These techniques provide input to an underlying preference model used by the navigation assistant to dynamically add or remove elements from the graph framework. Using the preference model, the assistant attempts to create a description of a user's preferences, possibly revealing previously unknown interests.
Legal Requirements Acquisition for the Specification of Legally Compliant Information Systems
(2009-04-22) Breaux, Travis; Jon Doyle, Committee Member; Eugene H. Spafford, Committee Member; Annie I. AntÃƒÂ³n, Committee Chair; David L. Baumer, Committee Member; Mladen A. Vouk, Committee Member
U.S. Federal and state regulations impose mandatory and discretionary requirements on industrywide business practices to achieve non-functional, societal goals such as improved accessibility, privacy and safety. The structure and syntax of regulations affects how well software engineers identify and interpret legal requirements. Inconsistent interpretations can lead to noncompliance and violations of the law. To support software engineers who must comply with these regulations, I propose a Frame-Based Requirements Analysis Method (FBRAM) to acquire and specify legal requirements from U.S. federal regulatory documents. The legal requirements are systematically specified using a reusable, domain-independent upper ontology, natural language phrase heuristics, a regulatory document model and a frame-based markup language. The methodology maintains traceability from regulatory statements and phrases to formal properties in a frame-based model and supports the resolution of multiple types of legal ambiguity. The methodology is supported by a software prototype to assist engineers with applying the model and with analyzing legal requirements. This work is validated in three domains, information privacy, information accessibility and aviation safety, which are governed by the Health Insurance Portability and Accountability Act of 1996, the Rehabilitation Act Amendments of 1998, and the Federal Aviation Act of 1958, respectively.
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.
On Graph Perturbation Theory and Algorithms for Scalable Mining of Noisy and Uncertain Graph Data with Knowledge Priors.
(2010-11-05) Hendrix, William; Nagiza Samatova, Committee Chair; Jon Doyle, Committee Member; Rada Chirkova, Committee Member; Anatoli Melechko, Committee Member
Pairwise Document Similarity using an Incremental Approach to TF-IDF.
(2010-08-09) Venkatesh, Jayashree; Christopher Healey, Committee Chair; Robert St. Amant, Committee Member; Jon Doyle, Committee Member
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.
Scalable Graph-Mining Techniques with Applications to Systems Biology.
(2010-12-10) Schmidt, Matthew; Anatoli Melechko, Committee Chair; Nagiza Samatova, Committee Chair; Donald Bitzer, Committee Member; Jon Doyle, Committee Member
Statistical Topics in Bioinformatics and Applications in Genome-Wide Association Studies.
(2010-04-08) Suchindran, Sunil; Shaobang Zeng, Committee Chair; Dahlia Nielsen, Committee Chair; Ron Sederoff, Committee Member; Jung-Ying Tzeng, Committee Member; Jon Doyle, Committee Member
To Read Images Not Words: Computer-Aided Analysis of the Handwriting in the Codex Seraphinianus.
(2010-10-05) Stanley, Jeffrey; Robert Rodman, Committee Chair; James Lester II, Committee Member; Jon Doyle, Committee Member
Trust and Reputation in Multiagent Systems: Strategies and Dynamics with Reference to Electronic Commerce.
(2010-05-04) Hazard, Christopher; Munindar Singh, Committee Chair; Robert Young, Committee Member; Jon Doyle, Committee Member; Ting Yu, Committee Member