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Journals Conferences Reports
Tegegne, T. and Weide, Th.P. van der, Enriching Queries with user preferences in healthcare. Information Processing & Management, Volume 50, Issue 4, July 2014, Pages 599-620
Query enrichment is a process of dynamically enhancing a user query based on her preferences and contexts in order to provide personalized answers. The central idea is that different users may find different services relevant to search due to different preferences and contexts. In this paper, we present a preference model that combines user's preferences, user's context, and domain knowledge to enrich the initial query of the user. Besides, we use CP-nets to rank the preferences using implicit and explicit user preferences and domain knowledge. Finally, we present a medical case study to validate the proposed conditional preferences model.
Blokpoel, M. and Kwisthout, J. and Weide, Th.P. van der and Wareham, T. and Rooij, I. van, A Computational-level Explanation of the Speed of Goal Inference. Journal of Mathematical Psychology, Volume 57, Issues 3-4, June-August 2013, Pages 117-133
The ability to understand the goals that drive another persons actions is an important social and cognitive skill. This is no trivial task, because any given action may in principle be explained by different possible goals (e.g., one may wave ones arm to hail a cab or to swat a mosquito). To select which goal best explains an observed action is a form of abduction. To explain how people perform such abductive inferences, Baker, Saxe and Tenenbaum (2009) proposed a computational-level theory that formalizes goal inference as Bayesian inverse planning (BIP). It is known that general Bayesian inference?be it exact or approximate?is computationally intractable (NP-hard). As the time required for computationally intractable computations grows excessively fast when scaled from toy domains to the real world, it seems that such models cannot explain how humans can perform Bayesian inferences quickly in real world situations. In this paper we investigate how the BIP model can nevertheless explain how people are able to make goal inferences quickly. The approach that we propose builds on taking situational constraints explicitly into account in the computational-level model. We present a methodology for identifying situational constraints that render the model tractable. We discuss the implications of our findings and reflect on how the methodology can be applied to alternative models of goal inference and Bayesian models in general.
Keywords: goal inference, abduction, inverse planning, computational complexity, intractability, NP-hard, fixed-parameter tractability
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Journals Conferences Reports
Oldenhave, D. and Hoppenbrouwers, S. and Weide, Th.P. van der and Lagarde, R., Gamification to Support the Run-Time Planning Process in Adaptive Case Management.
Adaptive Case Management is used to manage unpredictable processes. These processes are mostly knowledge-oriented and different roles need to collaborate to carefully plan the next steps during the execution of a case. These next steps cannot always be planned ahead, but depend on events and changes and differ from each instance. During the execution period the actual model of the run-time planning, of this particular instance of a case, is made. For different roles to jointly plan the correct next steps, it is important that such a case can be conceptualized so communication about the case becomes more easily. In this paper we suggest the idea of using game-elements, or Gamification, to enhance the planning process during the execution of such a case. With the use of Gamification we focus on this process by making it more recognizable for people and creating a greater involvement by reproducing the familiarity of games. The use of some kind of role-playing game is already being used for workshops and requirements elicitation, our suggestion of Gamification is a more modern approach. By building on existing work in Adaptive Case Management and Gamification we show that most games and the planning process of a case do not differ that much. More in particular, we will discuss how we can learn from games to improve the team play during the planning process of a case. Finally this idea will be explained by an example of a planning process of an unpredictable case.
Bagarukayo, E. and Semwangam, A.R. and Weide, Th.P. van der, Supporting the Learning of Secondary School Science Environment through Interactive Multimedia Technologies. ICTD2013: Paper 56
In this paper we report on a Ugandan initiative to establish the effectiveness of using multimedia, in particular, the multimedia case studies, in the classroom and how its use can effect and impact learning skills and teaching strategies particularly to enhance Higher Order Cognitive Skills (HOCS). The societal dilemma reflects on the many science students in Uganda and other developing countries today who lack competent Science teachers, materials and content as well as the technological background sought by contemporary employers: hence the need for courses and corresponding assessment methods to address this dilemma. Despite all the investments and continuing efforts of researchers, in developing and testing the evolving multimedia instructional technology in distance learning and classroom settings, there still is a paucity of research on the effectiveness and impact of this technology on teaching and learning. Data collected to assess improvement in students' learning based on pre- and post- test results will be used to create assessment tools and multimedia content to fit within topics covered in the science classes and uploaded on a web portal. Selected institutions will implement and evaluate the multimedia cases in science courses. The proposed research and education activities will further extend the usefulness of the multimedia case studies and fill the knowledge gap that previous research has identified with developers, instructors, researchers and students using the multimedia case studies.
Journals Conferences Reports
Tegegne, T. and Weide, Th.P. van der, Preference Prioritization and Aggregation in the Medical Domain.
In this paper we focus on the medical decision process. First we describe a general approach to multiple criteria decision making, with a special emphasis on the decomposition of such problems. We discuss two special approaches, the ordered weighting average and the analytical hierarchy process. Then we define the medical decision problem as a special case of multiple criteria decision making, in which we define three major subdecisions: diagnosis, risk handling and balancing. We pay special attention to personalization during balancing and show how personal preferences may lead to different treatments. This is applied on the medical diagnosis system TenaLeHulum that we introduced to be applied in low infrastructure situations such as is the case in developing countries. We show how our proposed model is being used in the context of treating HIV and malaria for pregnant women. Finally we discuss our next steps in developing TenaLeHulum.