@phdthesis{Wiebusch2016,
  author    = {Wiebusch, Dennis},
  title     = {Reusability for Intelligent Realtime Interactive Systems},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-040-5 (print)},
  doi       = {10.25972/WUP-978-3-95826-041-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-121869},
  school      = {W{\"u}rzburg University Press},
  pages     = {260},
  year      = {2016},
  abstract  = {Software frameworks for Realtime Interactive Systems (RIS), e.g., in the areas of Virtual, Augmented, and Mixed Reality (VR, AR, and MR) or computer games, facilitate a multitude of functionalities by coupling diverse software modules. In this context, no uniform methodology for coupling these modules does exist; instead various purpose-built solutions have been proposed. As a consequence, important software qualities, such as maintainability, reusability, and adaptability, are impeded. Many modern systems provide additional support for the integration of Artificial Intelligence (AI) methods to create so called intelligent virtual environments. These methods exacerbate the above-mentioned problem of coupling software modules in the thus created Intelligent Realtime Interactive Systems (IRIS) even more. This, on the one hand, is due to the commonly applied specialized data structures and asynchronous execution schemes, and the requirement for high consistency regarding content-wise coupled but functionally decoupled forms of data representation on the other. This work proposes an approach to decoupling software modules in IRIS, which is based on the abstraction of architecture elements using a semantic Knowledge Representation Layer (KRL). The layer facilitates decoupling the required modules, provides a means for ensuring interface compatibility and consistency, and in the end constitutes an interface for symbolic AI methods.},
  subject      = {Virtuelle Realit{\"a}t},
  language  = {en}
}
@phdthesis{Betz2005,
  author    = {Betz, Christian},
  title     = {Scalable authoring of diagnostic case based training systems},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-17885},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {Diagnostic Case Based Training Systems (D-CBT) provide learners with a means to learn and exercise knowledge in a realistic context. In medical education, D-CBT Systems present virtual patients to the learners who are asked to examine, diagnose and state therapies for these patients. Due a number of conflicting and changing requirements, e.g. time for learning, authoring effort, several systems were developed so far. These systems range from simple, easy-to-use presentation systems to highly complex knowledge based systems supporting explorative learning. This thesis presents an approach and tools to create D-CBT systems from existing sources (documents, e.g. dismissal records) using existing tools (word processors): Authors annotate and extend the documents to model the knowledge. A scalable knowledge representation is able to capture the content on multiple levels, from simple to highly structured knowledge. Thus, authoring of D-CBT systems requires less prerequisites and pre-knowledge and is faster than approaches using specialized authoring environments. Also, authors can iteratively add and structure more knowledge to adapt training cases to their learners needs. The theses also discusses the application of the same approach to other domains, especially to knowledge acquisition for the Semantic Web.},
  subject      = {Computerunterst{\"u}tztes Lernen},
  language  = {en}
}