TY - JOUR A1 - Mileo, Alessandra A1 - Schaub, Torsten H. A1 - Merico, Davide A1 - Bisiani, Roberto T1 - Knowledge-based multi-criteria optimization to support indoor positioning T2 - Annals of mathematics and artificial intelligence N2 - 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. KW - Knowledge representation KW - Answer Set Programming KW - Wireless Sensor Networks KW - Localization KW - Tracking Y1 - 2011 UR - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/36747 SN - 1012-2443 SN - 1573-7470 VL - 62 IS - 3-4 SP - 345 EP - 370 PB - Springer CY - Dordrecht ER -