We present a comparison of two new approaches for solving constraints occurring in spatial inference. In contrast to qualitative spatial reasoning we use a metric description, where relations between pairs of objects are represented by parameterized homogenous transformation matrices with numerical (nonlinear) constraints. We employ interval arithmetics based constraint solving and methods of machine learning in combination with a new algorithm for generating depictions for spatial inference.
CITATION STYLE
Gips, C., Hofstedt, P., & Wysotzki, F. (2002). Spatial inference – Learning vs. constraint solving. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2479, pp. 299–313). Springer Verlag. https://doi.org/10.1007/3-540-45751-8_20
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