Mobile Intention Recognition
Mobile Intention Recognition addresses problems of practical relevance for mobile system engineers: how can we make mobile assistance systems more intelligent? How can we model and recognize patterns of human behavior which span more than a limited spatia
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Peter Kiefer
Mobile Intention Recognition Foreword by Christoph Schlieder
Peter Kiefer University of Bamberg Bamberg, Germany [email protected]
This book has been accepted as a PhD thesis for a degree of Dr. rer.nat. by the Faculty of Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg (Germany), under the original title: The Mobile Intention Recognition Problem And An Approach Based On Spatially-Constrained Grammars”. ”
ISBN 978-1-4614-1853-5 e-ISBN 978-1-4614-1854-2 DOI 10.1007/978-1-4614-1854-2 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011942921 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
For my parents.
Foreword
Understanding what the user of a mobile information system plans to do next – recognizing his or her intentions – is of crucial importance for the design of location-based services (LBS). Solutions to the mobile intention recognition problem are especially needed in application scenarios where the opportunities to interact with the mobile device via a keyboard or a touchscreen are limited. This is the case for the use of smart-phones in many outdoor activities such as riding a bike, skiing, or running. Even hikers prefer not to stop to interact with the device. Ideally, the LBS would analyze the spatial behavior of the user, identify the user’s intentions to act, select the currently active intention, and finally provide the information services that assist the user in achieving the intended goal. In his thesis, Peter Kiefer provides the first comprehensive treatment of the field. This includes not only a thorough review of the state of the art but also a formal characterization of the mobile intention recognition problem as opposed to the general intention recognition problem. Although the approach is formal, it is deeply rooted in the practical experience that the author of this book gained while designing LBS, especially tourist guides and locationbased games. The challenge of intention recognition consists in finding an adequate approach for representing the background knowledge about the structure of the spatial environment (e.g. the partonomic structure of a city) and the spatiotemporally constrained
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