Sylvia Wiebrock

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We present an approach to spatial inference which is based on the procedural semantics of spatial relations. In contrast to qualitative reasoning, we do not use discrete symbolic models. Instead, relations between pairs of objects are represented by parameterized homogeneous transformation matrices with numerical constraints. A textual description of a(More)
We present an approach to spatial reasoning that is based on homogenous coordinate systems and their transformations. In contrast to qualitative approaches, spatial relations are not represented by symbolic expressions only but additionally by parameters with constraints, which are subsets of real numbers. Our work is based on the notion of mental models in(More)
In the spatial domain, the inclusion of an object in a region can be defined by inequations containing trigonometric expressions and several variables. Proving that a relation holds involves constraint solving. To circumvent the computational difficulties arising for these problems, we explore the applicablility of classification learning programs to this(More)
The paper represents first results on solving constraint nets consisting of equations and inequations containing trigonometric functions by methods of Machine Learning. Constraints of this type occur for example in planning movements of a mobile robot on a symbolic level in a workspace where obstacles or other " dangerous regions " have to be avoided.(More)
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