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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8023

Title: Mixed Initiative Ambient Environments: A Self-Learning System to Support User Tasks in Interactive Environments
Authors: VERPOORTEN, Kristof
LUYTEN, Kris
CONINX, Karin
Issue Date: 2007
Citation: Third Workshop on Context Awareness for Proactive Systems (CAPS 2007).
Abstract: In this paper we introduce a pro-active self-learning agent system that supports the user in an ambient intelligence environment. Interaction in an ambient intelligence environment is typically very complex and demands lots of attention of the end-user. In many cases it is extremely difficult for the user to evaluate what the consequences are from interactions accomplished in this space. In the system we present in this paper, an agent monitors the user's interactions with the environment to learn the user's expectations when certain activities are performed. Machine learning techniques allow the system to pro-actively react on behalf of the user. When the system is not entirely sure whether the user wants to execute a certain action, it will not execute it on his or her behalf, but makes it easier for the user to execute it himself by guiding the user through the options. This mixed initiative can help the user while not risking to lose his or her confidence by executing the wrong action on the user's behalf. The kind of system presented in this paper takes a lot of simple, often reoccurring work out of the user's hands.
URI: http://hdl.handle.net/1942/8023
ISBN: 978-0-9556240-0-1
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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