Pierfrancesco Veltri

The third answer set programming competition: preliminary report of the system competition track

Answer Set Programming is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems are compared in the nowadays customary ASP Competition. The Third ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a selected collection of declarative specifications of benchmark problems, taken from a variety of domains as well as real world applications, and instances thereof. The Competition ran on two tracks: the Model & Solve Competition, held on an open problem encoding, on an open language basis, and open to any kind of system based on a declarative specification paradigm; and the System Competition, held on the basis of fixed, public problem encodings, written in a standard ASP language. This paper briefly discuss the format and rationale of the System competition track, and preliminarily reports its results.

The ASP System DLV2

We introduce Open image in new window , a new Answer Set Programming (ASP) system. Open image in new window combines Open image in new window , a fully-compliant ASP-Core-2 grounder, with the well-assessed solver Open image in new window . Input programs may be enriched by annotations and directives that customize heuristics of the system and extend its solving capabilities. An empirical analysis conducted on benchmarks from past ASP competitions shows that Open image in new window outperforms the old Open image in new window system and is close to the state-of-the-art ASP system \(\textsc {clingo} \).

Magic-Sets for datalog with existential quantifiers

Datalog∃ is the extension of Datalog allowing existentially quantified variables in rule heads. This language is highly expressive and enables easy and powerful knowledge-modelling, but the presence of existentially quantified variables makes reasoning over Datalog∃ undecidable in the general case. Restricted classes of Datalog∃, such as shy, have been proposed in the literature with the aim of enabling powerful, yet decidable query answering on top of Datalog∃ programs. However, in order to make such languages attractive it is necessary to guarantee good performance for query answering tasks. This paper works in this direction: improving the performance of query answering on Datalog∃. To this end, we design a rewriting method extending the well-known Magic-Sets technique to any Datalog∃ program. We demonstrate that our rewriting method preserves query equivalence on Datalog∃, and can be safely applied to shy programs. We therefore incorporate the Magic-Sets method in DLV∃, a system supporting shy. Finally, we carry out an experiment assessing the positive impact of Magic-Sets on DLV∃, and the effectiveness of the enhanced DLV∃ system compared to a number of state-of-the-art systems for ontology-based query answering.

The ASP System DLV: Advancements and Applications

We briefly describe the answer set programming system DLV, focusing on some of its peculiar features and mentioning a number of successful applications.

A REST-Based Development Framework for ASP: Tools and Application

Answer Set Programming (ASP) is a declarative programming paradigm that has been successfully used in a number of industry-level applications also thanks to the availability of development tools. REpresentation State Transfer (REST) Web Services recently became a common and widely-used tool for enterprise applications. A service-oriented infrastructure for ASP would further catalyze the adoption of ASP-based solutions in real-world contexts. This paper introduces a REST-based framework for ASP, and reports on an application of the framework in the field of surveillance for photovoltaic plants.

Enhancing Existential Rules by Closed-World Variables

Existential rules generalize Datalog with existential quantification in the head. Natively, Datalog is interpreted under a closed-world semantics, while existential rules typically employ the open-world assumption. The interpretation domain in the latter case is enlarged by infinitely many “anonymous” individuals. Then, in any rule, each variable ranges over all individuals, even if not needed or required. In this paper, we enhance existential rules by closed-world variables to consciously reason on the properties of “known” (non-anonymous) and arbitrary individuals in different ways. Accordingly, we uniformly generalize the basic classes of existential rules that ensure decidability of ontologybased query answering. For them, after observing that decidability is preserved, we prove that a strict increase in expressiveness is gained, and in most cases the computational complexity is not altered.

Reasoning on anonymity in Datalog

In this paper we empower the ontology-based query answering framework with the ability to reason on the properties of “known” (non-anonymous) and anonymous individuals. To this end, we extend Datalog+/- with epistemic variables that range over “known” individuals only. The resulting framework, called datalog^{\exists,K}, offers good and novel knowledge representation capabilities, allowing for reasoning even on the anonymity of individuals. To guarantee effective computability, we define shyK, a decidable subclass of datalog^{\exists,K}, that fully generalizes (plain) Datalog, enhancing its knowledge modeling features without any computational overhead: OBQA for shyK keeps exactly the same (data and combined) complexity as for Datalog. To measure the expressiveness of shyK, we borrow the notion of uniform equivalence from answer set programming, and show that shyK is strictly more expressive than the DL ELH. Interestingly, shyK keeps a lower complexity, compared to other Datalog+/- languages that can express this DL.