Yeo-jin Kim

An adaptive user interface based on spatiotemporal structure learning

We developed a user interface prototype for the Android smartphone which recommends a number of applications to best match the users context. To consider the users context of use, we utilized 5 prototypical variables; time, location, weather, emotion, and activities. The developed system derives the best three recommended applications based on the results of supervised learning from such data sets. To consider the history of past context information, in addition to the current one, we developed a novel and effective probabilistic learning and inference algorithm named “Spatiotemporal Structure Learning”. By extending Naïve Bayesian Classifier, the spatiotemporal structure learning can create a probability model which represents relationship between time-series contextual variables. We implemented a prototype system which shows the current context and the inferred recommendation of applications. For the prototype system, we developed an Android widget application for the user interface and a Java-based server application which learns structure from training data and provides inference results in real-time. To gather training data and evaluate proposed system, we conducted a pilot study which showed 69% of accuracy in predicting the users application usage. The prototype demonstrated the feasibility of an adaptive user interface applied to a state of the art smartphone. We also believe that the suggested spatiotemporal structure learning can be applied to number of application areas including healthcare or energy problems.

An ontology-based reasoning approach towards energy-aware smart homes

We present an ontology-based reasoning approach for saving energy in a smart home setting where a mobile phone can serve as a generic sensor which can collect the inhabitants contextual data. The paper details an ontology that describes the smart home domain and a prototype to test the system. Finally, we conclude with lessons learned from our work in developing an energy-aware smart home prototype and suggestions for future work.

Planning-integrated story graph for interactive narratives

The advances in the interactive contents enable users to have a variety of experiences on diverse devices. In particular, two main approaches have been researched to construct digital interactive contents: a) conditional branch techniques and b) planning techniques. Each approach offers its own benefits; the conditional branch techniques allow the user to create tightly-plotted interactive contents; the planning techniques reduce the authors burden to specify every possible connection between contents considering the user input. As an attempt to combine these advantages provided by each technique, this paper discusses an interactive story structure incorporating the planning technique into the conditional branch techniques. Also, we briefly describe PRISM, a framework capable of creating and playing our story structure. We expect that the author can compose well-woven stories which can respond to a wide range of user interaction.

PRISM: A Framework for Authoring Interactive Narratives

The advances in computing technologies enable the computer users to create and share their own stories to the community at large. However, it is still regarded as complicated and laborious to author interactive narratives, where a story adapts as the user interacts with it. In authoring interactive narratives, two main approaches--branching graphs and AI planning--have been significantly used to augment interactivity into conventional linear narratives. Although each approach offers its own possibilities and limitations, few efforts have been made to blend these approaches. This paper describes a framework for authoring interactive narratives that employs an adapted branching narrative structure that also uses planning formalism to enable automated association between nodes. We expect that our work is valuable for non-expert users as well as AI researchers in interactive storytelling who need to create a large quantity of story contents for varied endings for a story.