Modeling the Progression of Alzheimer's Disease for Cognitive Assistance and Smart homes

International audienceSmart homes provide support to cognitively impaired people (such as those suffering from Alzheimer's disease) so that they can remain at home in an autonomous and safe way. In order to be efficient and responsive, cognitive assistance requires models of this impaired population. This paper presents a way to model and simulate the progression of dementia of the Alzheimer's type by evaluating performance in the execution of an activity of daily living (ADL). This model satisfies three objectives: first, it models an activity of daily living; second, it simulates the progression of the dementia and the errors potentially made by people suffering from it, and, finally, it simulates the support needed by the impaired person. To develop this model, we chose the ACT-R cognitive architecture, which uses symbolic and subsymbolic representations. The simulated results of 100 people suffering from Alzheimer's disease closely resemble the results obtained by 106 people on an occupational assessment (the Kitchen Task Assessment)

Can Genetic Algorithms Explain Experimental Anomalies?

In experimental data, it is common to find persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm (GA), where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments. Copyright Kluwer Academic Publishers 2004bounded rationality, common-pool resources, experiments, genetic algorithms,

User-adaptive explanatory program visualization: Evaluation and insights from eye movements

User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track users' progress allows students to interact with a larger amount of material than an application which does not follow users' activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the application's estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of students' attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities. © Springer Science+Business Media B.V. 2010