Michael Wißner

Multiple agent roles in an adaptive virtual classroom environment

We present the design of a cast of pedagogical agents impersonating different educational roles in an interactive virtual learning environment. Teams of those agents are used to create different learning scenarios in order to provide learners with an engaging and motivating learning experience. Authors can employ an easy to use multimodal dialog authoring tool to adapt lecture and dialog content as well as interaction management to meet their respective requirements.

DynaLearn: architecture and approach for investigating conceptual system knowledge acquisition

DynaLearn is an Interactive Learning Environment that facilitates a constructive approach to developing a conceptual understanding of how systems work The software can be put in different interactive modes facilitating alternative learning experiences, and as such provides a toolkit for educational research.

Trust-based Decision-making for the Adaptation of Public Displays in Changing Social Contexts

Public displays may adapt intelligently to the social context, tailoring information on the screen, for example, to the profiles of spectators, their gender or based on their mutual proximity. However, such adaptation decisions should on the one hand match user preferences and on the other maintain the user’s trust in the system. A wrong decision can negatively influence the user’s acceptance of a system, cause frustration and, as a result, make users abandon the system. In this paper, we propose a trust-based mechanism for automatic decision-making, which is based on Bayesian Networks. We present the process of network construction, initialization with empirical data, and validation. The validation demonstrates that the mechanism generates accurate decisions on adaptation which match user preferences and support user trust.

A User Trust Model for Automatic Decision-Making in Ubiquitous and Self-Adaptive Environments

Ubiquitous Environments are able to support users during their daily life by intelligently self-adapting to changed contexts. Examples include home automation systems which can support energy saving by switching off unused devices or public displays which enable users to present and interact with data, but maintain the users’ privacy by hiding sensible data if others pass by. However, such proactive adaptations could also cause frustration and thus harm the users’ acceptance and trust towards a system if they do not match the users’ preferences or are not self-explanatory. In the worst case, wrong or incomprehensible decisions by the system even could make the users abandon the system. To address this concern, we propose a generic trust-based model, called User Trust Model (UTM), which facilitates automatic decision-making in ubiquitous and self-adaptive environments. It is supposed to monitor users’ trust in the system and to select context-aware system actions that maintain, restore, or even foster user trust. In this chapter, the construction of the generic model as well as its integration into two case studies will be presented. We will provide a detailed description of how to customise the UTM for the respective scenarios and share results and experiences from various studies conducted with the developed systems.

Trust-Based Decision-Making for Energy-Aware Device Management

Smart energy systems are able to support users in saving energy by controlling devices, such as lights or displays, depending on context information, such as the brightness in a room or the presence of users. However, proactive decisions should also match the users’ preferences to maintain users’ trust in the system. Wrong decisions could negatively influence users’ acceptance of a system and at worst could make them abandon the system. In this paper, a trust-based model, called User Trust Model (UTM), for automatic decision-making is proposed, which is based on Bayesian Networks. The UTM’s construction, the initialization with empirical data gathered in an online survey, and its integration in an office setting are described. Furthermore, the results of a user study investigating users’ experience and acceptance are presented.

Visual communication in interactive multimedia

Careful selection of graphical design can push the narrative strength of graphical projects by adjusting the visual statements to the content-wise statements. While many projects of computer science lack of consequent implementation of artistic principles, graphic designers tend to neglect user interaction and evaluation. In a recent project we therefore started a successful approach to combine both sides. In future work we plan to research on further integration of visual narration into interactive storytelling.

Adaptive art --- a shape language driven approach to communicate dramaturgy and mood

Graphic designers and visual artists express their narrative intentions using visual tools which essentially include the styling of shapes. In classic art forms design decisions are limited to the initial creation process. Although technical progress makes it possible to adjust content-wise dramaturgy in real-time, the graphical design process often still has static characteristics. In our current work we therefore study how dramaturgical non-linearity can be combined with a flexible visual shape language to conform content-wise narration to visual expressiveness in real-time.

Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

This paper presents a domain independent question generation and interaction procedure that automatically generates multiple-choice questions for conceptual models created with Qualitative Reasoning vocabulary. A Bayesian Network is deployed that captures the learning progress based on the answers provided by the learner. The likelihood of concepts being known or unknown on behalf of the learner determines the focus, and the question generator adjusts the contents of its questions accordingly. As a use case, the Quiz mode is introduced.

Knowledgeable feedback via a cast of virtual characters with different competences

DynaLearn (http://www.DynaLearn.eu) develops a cognitive artefact that engages learners in an active learning by modelling process to develop conceptual system knowledge. Learners create external representations using diagrams. The diagrams capture conceptual knowledge using the Garp3 Qualitative Reasoning (QR) formalism [2]. The expressions can be simulated, confronting learners with the logical consequences thereof. To further aid learners, DynaLearn employs a sequence of knowledge representations (Learning Spaces, LS), with increasing complexity in terms of the modelling ingredients a learner can use [1]. An online repository contains QR models created by experts/teachers and learners. The server runs semantic services [4] to generate feedback at the request of learners via the workbench. The feedback is communicated to the learner via a set of virtual characters, each having its own competence [3]. A specific feedback thus incorporates three aspects: content, character appearance, and a didactic setting (e.g. Quiz mode). In the interactive event we will demonstrate the latest achievements of the DynaLearn project. First, the 6 learning spaces for learners to work with. Second, the generation of feedback relevant to the individual needs of a learner using Semantic Web technology. Third, the verbalization of the feedback via different animated virtual characters, notably: Basic help, Critic, Recommender, Quizmaster & Teachable agent.

Acquiring conceptual knowledge about how systems behave

There is a need for software that supports learners in actively dealing with theoretical concepts by having them create models and perform concept prediction and explanation (e.g [3,4,5]) DynaLearn seeks to address this by developing a domain independent Interactive Learning Environment (ILE) based on Qualitative Reasoning (QR) [1] The QR vocabulary fits the nature of conceptual knowledge, and the explicit representation of these notions in the software provides the handles to support an automated communicative interaction that actually discusses and provides feedback at the conceptual level.