The problem of grasping is widely studied in the The problem of semantic part decomposition is still an robotics community. This project focuses on the open problem and, to the best of our knowledge, there are identification of object graspable features using images no tools available to automatically create a fuzzy ontology and object structural information. The primary from raw data taken from an image. The use of fuzzy DLs for aim is the creation of a framework in which the information object recognition has been investigated in some works such gathered by the vision system can be integrated as [Hudelot et al., 2008], in which little advantage is taken with automatically generated knowledge, from the (partial) fuzzy extension and from the expressivity modelled by means of fuzzy description logics. of the used logic (i.e., no cardinality restrictions are used); furthermore, a preliminary phase of semantic annotation of the images by domain experts has to be performed.

We characterize the expressive power of description logic (DL) TBoxes, both for expressive DLs such as ALC and ALCQIO and lightweight DLs such as DL-Lite and EL. Our characterizations are relative to first-order logic, based on a wide range of semantic notions such as bisimulation, equisimulation, disjoint union, and direct product. We exemplify the use of the characterizations by a first study of the following novel family of decision problems: given a TBox T formulated in one DL, decide whether T can be equivalently rewritten as a TBox in der fragment L' of L.

We propose a nonmonotonic extension of low complexity Description Logics EL⊥ and DL-Litecore for reasoning about typicality and defeasible properties. The resulting logics are called EL⊥ T min and DL-Litec T min . Concerning DL-Litec T min , we prove that entailment is in \Pi^p_2. With regard to EL⊥ T min , we first show that entailment remains EXPTIME-hard. Next we consider the known fragment of Left Local EL⊥ T min and we prove that the complexity of entailment drops to \Pi^p_2.

The automata-based approach is based on translating a knowledge base (KB) whose satisfiability is to be checked In this work we describe the theoretical foundations into some variant of automata on infinite trees that accepts and the implementation of a new automata-based tree-shaped models of the KB, and checking such an automaton technique for reasoning over expressive Description for non-emptiness. This approach is powerful and flexible. Logics that is worst-case optimal and lends itself It is acknowledged that it provides a very robust basis to an efficient implementation. In order to show for showing worst-case optimal complexity upper bounds, the feasibility of the approach, we have realized a and has been applied for a wide range of expressive DLs working prototype of a reasoner based upon these and reasoning services (cf.

We study the problem of dealing with inconsistency in Description Logic (DL) ontologies. We consider inconsistency-tolerant semantics recently proposed in the literature, called AR-semantics and CAR-semantics, which are based on repairing (i.e., modifying) in a minimal way the extensional knowledge (ABox) while keeping the intensional knowledge (TBox) untouched. We study instance checking and conjunctive query entailment under the above inconsistency-tolerant semantics for a wide spectrum of DLs, ranging from tractable ones (EL) to very expressive ones (SHIQ), showing that reasoning under the above semantics is inherently intractable, even for very simple DLs. To the aim of overcoming such a high computational complexity of reasoning, we study sound approximations of the above semantics. Surprisingly, our computational analysis shows that reasoning under the approximated semantics is intractable even for tractable DLs. Finally, we identify suitable language restrictions of such DLs allowing for tractable reasoning under inconsistency-tolerant semantics.

Log-linear description logics are a family of probabilistic logics integrating various concepts and methods from the areas of knowledge representation and reasoning and statistical relational AI. We define the syntax and semantics of log-linear description logics, describe a convenient representation as sets of first-order formulas, and discuss computational and algorithmic aspects of probabilistic queries in the language. The paper concludes with an experimental evaluation of an implementation of a log-linear DL reasoner.

We study a decidable fixpoint extension of temporal description logics. To this end we employ and extend decidability results obtained for various temporally first-order monodic extensions of (first-order) description logics. Using these techniques we obtain decidability and tight complexity results for various fixpoint extensions of temporal description logics.

Uncertainty and vagueness are pervasive phenomena in real-life knowledge. They are supported in extended description logics that adapt classical description logics to deal with numerical probabilities or fuzzy truth degrees. While the two concepts are distinguished for good reasons, they combine in the notion of probably, which is ultimately a fuzzy qualification of probabilities. Here, we develop existing propositional logics of fuzzy probability into a full-blown description logic, and we show decidability of several variants of this logic under Lukasiewicz semantics. We obtain these results in a novel generic framework of fuzzy coalgebraic logic; this enables us to extend our results to logics that combine crisp ingredients including standard crisp roles and crisp numerical probabilities with fuzzy roles and fuzzy probabilities.

Query containment has been studied extensively in KR and databases, for different kinds of query languages and domain constraints. We address the longstanding open problem of containment under expressive description logic (DL) constraints for two-way regular path queries (2RPQs) and their conjunctions, which generalize conjunctive queries with the ability to express regular navigation. We show that, surprisingly, functionality constraints alone make containment of 2RPQs already ExpTime-hard. By employing automata-theoretic techniques, we also provide a matching upper bound that extends to very expressive DL constraints. For conjunctive 2RPQs we prove a further exponential jump in complexity, and provide again a matching upper bound for expressive DLs. Our techniques provide also a solution to the problem of query entailment over DL knowledge bases in which individuals in the ABox may be related through regular role-paths.

Defeasible inheritance networks are a non-monotonic framework that deals with hierarchical knowledge. On the other hand, rational closure is acknowledged as a landmark of the preferential approach. We will combine these two approaches and define a new non-monotonic closure operation for propositional knowledge bases that combines the advantages of both. Then we redefine such a procedure for Description Logics, a family of logics well-suited to model structured information. In both cases we will provide a simple reasoning method that is build on top of the classical entailment relation.