Stochastic Activity Authoring With Direct User Control

Crowd activities are often randomized to create the appearance of heterogeneity. However, the parameters that control randomization are frequently hard to tune because it is unclear how changes at the character level affect the high-level appearance of the crowd. We propose a method for computing randomization parameters that supports direct animator control. Given details about the environment, available activities, timing information and the desired highlevel appearance of the crowd, we model the problem as a graph, formulate a convex optimization problem, and solve for a set of stochastic transition rates which satisfy the constraints. Unlike the use of heuristics for adding randomness to crowd activities, our approach provides guarantees on convergence to the desired result, allows for decentralized simulation, and supports a variety of constraints. In addition, because the rates can be pre-computed, no additional runtime processing is needed during simulation

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

Iterchanging Discrete Event Simulationprocess Interaction Modelsusing The Web Ontology Language - Owl

Discrete event simulation development requires significant investments in time and resources. Descriptions of discrete event simulation models are associated with world views, including the process interaction orientation. Historically, these models have been encoded using high-level programming languages or special purpose, typically vendor-specific, simulation languages. These approaches complicate simulation model reuse and interchange. The current document-centric World Wide Web is evolving into a Semantic Web that communicates information using ontologies. The Web Ontology Language OWL, was used to encode a Process Interaction Modeling Ontology for Discrete Event Simulations (PIMODES). The PIMODES ontology was developed using ontology engineering processes. Software was developed to demonstrate the feasibility of interchanging models from commercial simulation packages using PIMODES as an intermediate representation. The purpose of PIMODES is to provide a vendor-neutral open representation to support model interchange. Model interchange enables reuse and provides an opportunity to improve simulation quality, reduce development costs, and reduce development times

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

Scalable Exploration of Complex Objects and Environments Beyond Plain Visual Replication​

Digital multimedia content and presentation means are rapidly increasing their sophistication and are now capable of describing detailed representations of the physical world. 3D exploration experiences allow people to appreciate, understand and interact with intrinsically virtual objects. Communicating information on objects requires the ability to explore them under different angles, as well as to mix highly photorealistic or illustrative presentations of the object themselves with additional data that provides additional insights on these objects, typically represented in the form of annotations. Effectively providing these capabilities requires the solution of important problems in visualization and user interaction. In this thesis, I studied these problems in the cultural heritage-computing-domain, focusing on the very common and important special case of mostly planar, but visually, geometrically, and semantically rich objects. These could be generally roughly flat objects with a standard frontal viewing direction (e.g., paintings, inscriptions, bas-reliefs), as well as visualizations of fully 3D objects from a particular point of views (e.g., canonical views of buildings or statues). Selecting a precise application domain and a specific presentation mode allowed me to concentrate on the well defined use-case of the exploration of annotated relightable stratigraphic models (in particular, for local and remote museum presentation). My main results and contributions to the state of the art have been a novel technique for interactively controlling visualization lenses while automatically maintaining good focus-and-context parameters, a novel approach for avoiding clutter in an annotated model and for guiding users towards interesting areas, and a method for structuring audio-visual object annotations into a graph and for using that graph to improve guidance and support storytelling and automated tours. We demonstrated the effectiveness and potential of our techniques by performing interactive exploration sessions on various screen sizes and types ranging from desktop devices to large-screen displays for a walk-up-and-use museum installation. KEYWORDS - Computer Graphics, Human-Computer Interaction, Interactive Lenses, Focus-and-Context, Annotated Models, Cultural Heritage Computing

Real-Time Storytelling with Events in Virtual Worlds

We present an accessible interactive narrative tool for creating stories among a virtual populace inhabiting a fully-realized 3D virtual world. Our system supports two modalities: assisted authoring where a human storyteller designs stories using a storyboard-like interface called CANVAS, and exploratory authoring where a human author experiences a story as it happens in real-time and makes on-the-fly narrative trajectory changes using a tool called Storycraft. In both cases, our system analyzes the semantic content of the world and the narrative being composed, and provides automated assistance such as completing partially-specified stories with causally complete sequences of intermediate actions. At its core, our system revolves around events -â?? pre-authored multi-actor task sequences describing interactions between groups of actors and props. These events integrate complex animation and interaction tasks with precision control and expose them as atoms of narrative significance to the story direction systems. Events are an accessible tool and conceptual metaphor for assembling narrative arcs, providing a tightly-coupled solution to the problem of converting author intent to real-time animation synthesis. Our system allows simple and straightforward macro- and microscopic control over large numbers of virtual characters with diverse and sophisticated behavior capabilities, and reduces the complicated action space of an interactive narrative by providing analysis and user assistance in the form of semi-automation and recommendation services

BeSocratic: An Intelligent Tutoring System for the Recognition, Evaluation, and Analysis of Free-form Student Input

This dissertation describes a novel intelligent tutoring system, BeSocratic, which aims to help fill the gap between simple multiple-choice systems and free-response systems. BeSocratic focuses on targeting questions that are free-form in nature yet defined to the point which allows for automatic evaluation and analysis. The system includes a set of modules which provide instructors with tools to assess student performance. Beyond text boxes and multiple-choice questions, BeSocratic contains several modules that recognize, evaluate, provide feedback, and analyze student-drawn structures, including Euclidean graphs, chemistry molecules, computer science graphs, and simple drawings. Our system uses a visual, rule-based authoring system which enables the creation of activities for use within science, technology, engineering, and mathematics classrooms. BeSocratic records each action that students make within the system. Using a set of post-analysis tools, teachers have the ability to examine both individual and group performances. We accomplish this using hidden Markov model-based clustering techniques and visualizations. These visualizations can help teachers quickly identify common strategies and errors for large groups of students. Furthermore, analysis results can be used directly to improve activities through advanced detection of student errors and refined feedback. BeSocratic activities have been created and tested at several universities. We report specific results from several activities, and discuss how BeSocratic\u27s analysis tools are being used with data from other systems. We specifically detail two chemistry activities and one computer science activity: (1) an activity focused on improving mechanism use, (2) an activity which assesses student understanding of Gibbs energy, and (3) an activity which teaches students the fundamentals of splay trees. In addition to analyzing data collected from students within BeSocratic, we share our visualizations and results from analyzing data gathered with another educational system, PhET