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Devaurs Didier, Rath Andreas S., Lindstaedt Stefanie

Exploiting the User Interaction Context for Automatic Task Detection

Applied Artificial Intelligence, Taylor & Francis Group, 2012

Detecting the task a user is performing on his/her computer desktop is important in order to provide him/her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (1) capturing simple user interaction events on the computer desktop and (2) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task-detection performance. We also argue that good results can be achieved by training task classifiers “offline” on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones.

Rath Andreas S., Devaurs Didier, Lindstaedt Stefanie

An Ontology-Based Approach for Detecting Knowledge Intensive Tasks

Journal of Digital Information Management, Pichappan, P., Jacobs, D. , Digital Information Research Foundation, 2011

In the context detection field, an important challenge is automatically detecting the user's task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.

Devaurs Didier, Gras R.

Species Abundance Patterns in an Ecosystem Simulation Studied through Fisher's Logseries

Simulation Modelling Practice and Theory, Elsevier, 2010

We have developed an individual-based evolving predator–prey ecosystem simulation thatintegrates, for the first time, a complex individual behaviour model, an evolutionary mechanismand a speciation process, at an acceptable computational cost. In this article, we analysethe species abundance patterns observed in the communities generated by oursimulation, based on Fisher’s logseries. We propose a rigorous methodology for testingabundance data against the logseries. We show that our simulation produces coherentresults, in terms of relative species abundance, when compared to classical ecological patterns.Some preliminary results are also provided about how our simulation is supportingecological field results.

Granitzer Michael, Rath Andreas S., Kröll Mark, Ipsmiller D., Devaurs Didier, Weber Nicolas, Lindstaedt Stefanie , Seifert C.

Machine Learning based Work Task Classification

Journal of Digital Information Management, 2009

Increasing the productivity of a knowledgeworker via intelligent applications requires the identification ofa user’s current work task, i.e. the current work context a userresides in. In this work we present and evaluate machine learningbased work task detection methods. By viewing a work taskas sequence of digital interaction patterns of mouse clicks andkey strokes, we present (i) a methodology for recording thoseuser interactions and (ii) an in-depth analysis of supervised classificationmodels for classifying work tasks in two different scenarios:a task centric scenario and a user centric scenario. Weanalyze different supervised classification models, feature typesand feature selection methods on a laboratory as well as a realworld data set. Results show satisfiable accuracy and high useracceptance by using relatively simple types of features.

Gras R., Devaurs Didier, Wozniak A., Aspinall A.

An Individual-Based Evolving Predator-Prey Ecosystem Simulation Using a Fuzzy Cognitive Map as the Behavior Model

Artificial Life, Massachusetts Institute of Technology, 2009

We present an individual-based predator-prey model with, for the first time, each agent behavior being modeled by a fuzzy cognitive map (FCM), allowing the evolution of the agent behavior through the epochs of the simulation. The FCM enables the agent to evaluate its environment (e.g., distance to predator or prey, distance to potential breeding partner, distance to food, energy level) and its internal states (e.g., fear, hunger, curiosity), and to choose several possible actions such as evasion, eating, or breeding. The FCM of each individual is unique and is the result of the evolutionary process. The notion of species is also implemented in such a way that species emerge from the evolving population of agents. To our knowledge, our system is the only one that allows the modeling of links between behavior patterns and speciation. The simulation produces a lot of data, including number of individuals, level of energy by individual, choice of action, age of the individuals, and average FCM associated with each species. This study investigates patterns of macroevolutionary processes, such as the emergence of species in a simulated ecosystem, and proposes a general framework for the study of specific ecological problems such as invasive species and species diversity patterns. We present promising results showing coherent behaviors of the whole simulation with the emergence of strong correlation patterns also observed in existing ecosystems.
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