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We present two approaches to modeling affordance relations between objects, actions and effects. The first approach we present focuses on a probabilistic approach which uses a voting function to learn which objects afford which types of grasps. We compare the success rate of this approach to a second approach which uses an ontological reasoning engine for learning affordances. Our second approach employs a rule-based system with axioms to reason on grasp selection for a given object.