Abductive Logic Programming for Normative Reasoning and Ontologies

Abstract

Abductive Logic Programming (ALP) has been exploited to\ud formalize societies of agents, commitments and norms, taking advantage from ALP operational support as a (static or dynamic) verification\ud tool. In [7], the most common deontic operators (obligation, prohibition, permission) are mapped into the abductive expectations of an ALP\ud framework for agent societies. Building upon such correspondence, in [5],\ud authors introduced Deon+ , a language where obligation and prohibition\ud deontic operators are enriched with quantification over time, by means\ud of ALP and Constraint Logic Programming (CLP).\ud In recent work [30, 31], we have shown that the same ALP framework\ud can be suitable to represent Datalog± ontologies. Ontologies are a fundamental component of both the Semantic Web and knowledge-based\ud systems, even in the legal setting, since they provide a formal and machine manipulable model of a domain.\ud In this work, we show that ALP is a suitable framework for representing\ud both norms and ontologies. Normative reasoning and ontological query\ud answering are obtained by applying the same abductive proof procedure,\ud smoothly achieving their integration. In particular, we consider the ALP\ud framework named SCIFF and derived from the IFF abductive framework, able to deal with existentially (and universally) quantified variables\ud in rule heads and CLP constraints.\ud The main advantage is that this integration is achieved within a single\ud language, grounded on abduction in computational logic

A web application for reasoning on probabilistic description logics knowledge bases

“…The W3 Consortium proposed a document for “potential uses of the Semantic Web in Systems and Software Engineering”. Ontologies have been used also in the legal domain 7 . We believe that all these fields could benefit from an uncertain representation, as well as, for example, fault diagnosis or recommender systems, where probability could be used, in the former case, to calculate a fault risk value, showing whether a production line could be affected by a fault and, in the latter case, to rank possible recommendations.…”

“…* Ontologies have been used also in the legal domain. 7 We believe that all these fields could benefit from an uncertain representation, as well as, for example, fault diagnosis or recommender systems, where probability could be used, in the former case, to calculate a fault risk value, showing whether a production line could be affected by a fault and, in the latter case, to rank possible recommendations. These needs motivated many researchers to study and present probabilistic semantics capable of handling the uncertainty inherent in the Semantic Web.…”

Dischargeable Obligations in the 𝒮CIFF Framework

“…ALP is a powerful tool for knowledge representation and reasoning [18], for example it has been applied to represent ontologies [19,20]. ALP is based on a declarative (model-theoretic) semantics and equipped with an operational semantics in terms of a proof-procedure.…”

“…SCIFF [14] extends IFF to deal with constraints à la Constraint Logic Programming (CLP) [22], optimization meta-predicates [23] and with both existentially and universally quantified variables in rule heads. The resulting system was used for modeling and implementing several knowledge representation frameworks, such as normative systems [24], accountable protocols for multi-agent systems [25], web service choreographies [26], and Datalog ± ontologies [27,28]. In particular, SCIFF is particularly well suited for reasoning about norms because it features a concept of expected behavior, which is represented as a set of abducible atoms called expectations and produced by an abductive program reasoning on the current state of affairs (e.g., the current set of happened events).…”

Legal Reasoning and Big Data: Opportunities and Challenges

“…Since the emergence of computational knowledge representation and reasoning (KR), the domain of law has been a prime focus of attention as it is a rich domain full of explicit and implicit representation phenomena. From early Prolog-based approaches [40,42] to elaborate logic-based mechanisms for dealing with, among others, notions of defeasibility, obligation and permission, the legal domain has been an inspiration for generations of KR researchers [1,14,26,43].…”