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One of the key reasoning tasks of robotic agents is inferring possible actions that can be accomplished with a given object at hand. This cognitive task is commonly referred to as inferring the affordances of objects. In this paper, we propose a novel conceptualization of affordances and its realization as a description logic ontology. The key idea of the framework is that it proposes candidate affordances through inference, and that these can then be validated through physics-based simulation. We showcase the practical use of our conceptualization by means of demonstrating what competency questions an agent equipped with it can answer. The proposed formal model is implemented as a TBox OWL ontology of affordances based on the DOLCE Ultra Light + DnS foundational ontology.
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