Using f-SHIN to represent objects: an aid to visual grasping Nicola Vitucci, Mario Arrigoni Neri, and Giuseppina Gini Politecnico di Milano - Dipartimento di Elettronica e Informazione Via Ponzio 34/5, 20133 Milano, Italy {vitucci,arrigoni,gini}@elet.polimi.it Abstract. Description Logics (DLs) are nowadays used to face a va- riety of problems. When dealing with numerical data coming from the real world, however, the use of traditional logics results in a loss of useful information that can be otherwise exploited using more expressive log- ics. Fuzzy extensions of traditional DLs, being able to represent vague concepts, are well suited to reason on such objects. In this paper we present an architecture for the automatic building and querying of a fuzzy ontology related to the representation of objects in terms of their composing parts. Our approach mainly aims to face the problem of visual grasping, which is of wide interest in the robotics field. 1 Introduction The decomposition of an object in parts has been recognized as an important problem in artificial intelligence: it is considered both as a human-like way of reasoning on objects [1] and as a good way to reduce complexity in tasks like object recognition [14]. Apart of the actual image decomposition phase, a major issue is constituted by the semantic description of the extracted features and their mutual relationships. Due to the vagueness affecting real world data, some tolerance should be taken into account when formally representing the structure of an object; this is a reason to take advantage of novel tools as fuzzy DLs [11]. Fuzzy DLs extend crisp DLs by adding imprecision and vagueness in the reasoning process, thus giving some degrees of truth in place of binary answers as yes or no. Although the available fuzzy reasoners are not yet as powerful as their crisp counterparts, some interesting applications can be found. One of them lies in the robotics field, in which a symbolic representation of objects can improve the grasping capabilities of a robot by the use of some semantic information, regarding both the type of grasp itself and the structure of the object to be grasped. To the best of our knowledge, the problem of semantic part decomposition is still an open problem and there are no tools available to automatically create a fuzzy ontology from raw concepts. The use of ontologies for object recognition has been investigated in some works as [4,5,6], but none of them makes explicitly use of fuzzy reasoning except for the creation of (crisp) descriptors as Very high to be