Publication

Knowledge-based multi-criteria optimization to support indoor positioning

Mileo, Alessandra
Schaub, Torsten
Merico, Davide
Bisiani, Roberto
Citation
Mileo, Alessandra; Schaub, Torsten; Merico, Davide; Bisiani, Roberto (2011). Knowledge-based multi-criteria optimization to support indoor positioning. Annals of Mathematics and Artificial Intelligence 62 (3), 345-370
Abstract
Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting.
Funder
Publisher
Springer Nature
Publisher DOI
10.1007/s10472-011-9241-2
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland