An ASP approach for reasoning in a concept-Aware multipreferential lightweight DL

In this paper we develop a concept aware multi-preferential semantics for dealing with typicality in description logics, where preferences are associated with concepts, starting from a collection of ranked TBoxes containing defeasible concept inclusions. Preferences are combined to define a preferential interpretation in which defeasible inclusions can be evaluated. The construction of the concept-Aware multipreference semantics is related to Brewka's framework for qualitative preferences. We exploit Answer Set Programming (in particular, asprin) to achieve defeasible reasoning under the multipreference approach for the lightweight description logic EL^+ bo

ASP and ontologies for reasoning on business processes

In this paper we show that Answer Set Programming (ASP) can accommodate for domain ontologies in modeling and reasoning about Business Processes, especially for process verification. In this work, knowledge on the process domain is expressed in a low-complexity description logic (DL), and terms from the ontology can be used in embedding business rules in the model as well as in expressing constraints that should be verified to achieve compliance by design. Causal rules for reasoning on side-effects of activities in the process domain can be derived, based on knowledge expressed in the DL. We show how ASP can accommodate them, relying on reasoning about actions and change, for process analysis, and, in particular, for verifying formulas in temporal logic

Weighted Defeasible Knowledge Bases and a Multipreference Semantics for a Deep Neural Network Model

In this paper we investigate the relationships between a multipreferential semantics for defeasible reasoning in knowledge representation and a deep neural network model. Weighted knowledge bases for description logics are considered under a \u201cconcept-wise\u201d multipreference semantics. The semantics is further extended to fuzzy interpretations and exploited to provide a preferential interpretation of Multilayer Perceptrons, under some condition

Focusing Abductive Diagnosis

The aim of this paper is to present a novel approach to the problem of focusing abductive (model-based) diagnosis. The approach we propose is based on the use of compiled knowledge and, specifically, on the possibility of associating with each entity in a model a necessary condition for the presence of the entity itself. Such conditions embed the problem solving strategy and their evaluation on the data characterizing the problem to be solved allows us to prune the search space, yet preserving the completeness of the abductive process (in other words, only useless search is avoided). The use of compiled knowledge thus allows us to mitigate the problems arising from the computational complexity of the model based approach. The final part of the paper is devoted to a comparison of our approach with other ones and to a brief discussion on the role of knowledge compilation in model-based diagnosis