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Apeldoorn, Netherlands

Both F.,VU University Amsterdam | Hoogendoorn M.,VU University Amsterdam | Van Der Mee A.,CAMS Force Vision | Treur J.,VU University Amsterdam | De Vos M.,CAMS Force Vision
Applied Intelligence | Year: 2012

To support human functioning, ambient intelligent agents require knowledge about the tasks executed by the human. This knowledge includes design-time information like: (i) the goal of a task and (ii) the alternative ways for a human to achieve that goal, as well as run-time information such as the choices made by a human during task execution. In order to provide effective support, the agent must know exactly what steps the human is following. However, if not all steps along the path can be observed, it is possible that the agent cannot uniquely derive which path the human is following. Furthermore, in order to provide timely support, the agent must observe, reason, conclude and support within a limited period of time. To deal with these problems, this paper presents a generic focused reasoning mechanism to enable a guided selection of the path which is most likely followed by the human. This mechanism is based upon knowledge about the human and the workflow to perform the task. In order to come to such an approach, a reasoning mechanism is adopted in combination with the introduction of a new workflow representation, which is utilized to focus the reasoning process in an appropriate manner. The approach is evaluated by means of an extensive case study. © Springer Science+Business Media, LLC 2010.

Bosse T.,VU University Amsterdam | Both F.,VU University Amsterdam | Duell R.,Ministry of Defense | Hoogendoorn M.,VU University Amsterdam | And 7 more authors.
International Journal of Intelligent Information and Database Systems | Year: 2013

Human task performance varies depending on the task, environment, and states of the human over time. To ensure high effectiveness and efficiency in the execution of complex tasks, adaptive automated assistance of the human may be required. In this paper, a generic design for a multi-agent system architecture is presented and a personal assistant agent is described that makes use of the proposed architecture. The agent constantly monitors the task execution and well-being of the human via non-intrusive sensors, and intervenes when a problem is detected. A human is given a complex task, while the future performance is predicted using observations and a dynamical model for the human's work pressure and exhaustion. If the predicted exhaustion becomes too high, the ambient agent can assist the human in a number of ways. Experiments with humans show that the support system increases performance with around 13%, and that it enhances the feeling of control of the situation. Copyright © 2013 Inderscience Enterprises Ltd.

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