We present plingo, an extension of the answer set programming (ASP) system clingo that incorporates various probabilistic reasoning modes. Plingo is based on $\textit{Lpmln}^{\pm }$, a simple variant of the probabilistic language Lpmln, which follows a weighted scheme derived from Markov logic. This choice is motivated by the fact that the main probabilistic reasoning modes can be mapped onto enumeration and optimization problems and that $\textit{Lpmln}^{\pm }$ may serve as a middle-ground formalism connecting to other probabilistic approaches. Plingo offers three alternative frontends, for Lpmln, P-log, and ProbLog. These input languages and reasoning modes are implemented by means of clingo’s multi-shot and theory-solving capabilities. In this way, the core of plingo is an implementation of $\textit{Lpmln}^{\pm }$ in terms of modern ASP technology. On top of that, plingo implements a new approximation technique based on a recent method for answer set enumeration in the order of optimality. Additionally, in this work, we introduce a novel translation from $\textit{Lpmln}^{\pm }$ to ProbLog. This leads to a new solving method in plingo where the input program is translated and a ProbLog solver is executed. Our empirical evaluation shows that the different solving approaches of plingo are complementary and that plingo performs similarly to other probabilistic reasoning systems.

In the wake of the recent resurgence of the Datalog language of databases, together with its extensions for ontological reasoning settings, this work aims to bridge the gap between the theoretical studies of DatalogMTL (Datalog extended with metric temporal logic) and the development of production-ready reasoning systems. In particular, we lay out the functional and architectural desiderata of a modern reasoner and propose our system, Temporal Vadalog. Leveraging the vast amount of experience from the database community, we go beyond the typical chase-based implementations of reasoners, and propose a set of novel techniques and a system that adopts a modern data pipeline architecture. We discuss crucial architectural choices, such as how to guarantee termination when infinitely many time intervals are possibly generated, how to merge intervals, and how to sustain a limited memory footprint. We discuss advanced features of the system, such as the support for time series, and present an extensive experimental evaluation. This paper is a substantially extended version of “The Temporal Vadalog System” as presented at RuleML+RR ’22.

The representation of a temporal problem in answer set programming (ASP) usually boils down to using copies of variables and constraints, one for each time stamp, no matter whether it is directly encoded or expressed via an action or temporal language. The multiplication of variables and constraints is commonly done during grounding, and the solver is completely ignorant about the temporal relationship among the different instances. On the other hand, a key factor in the performance of today’s ASP solvers is conflict-driven constraint learning. Our question in this paper is whether a constraint learned for particular time steps can be generalized and reused at other time steps, and ultimately whether this enhances the overall solver performance on temporal problems. Knowing well the domain of time, we study conditions under which learned dynamic constraints can be generalized. Notably, we identify a property of temporal representations that enables the generalization of learned constraints across all time steps. It turns out that most ASP planning encodings either satisfy this property or can be easily adapted to do so. In addition, we propose a general translation that transforms programs to fulfill this property. Finally, we empirically evaluate the impact of adding the generalized constraints to an ASP solver.

DatalogMTL is an extension of Datalog with metric temporal operators that has found an increasing number of applications in recent years. Reasoning in DatalogMTL is, however, of high computational complexity, which makes reasoning in modern data-intensive applications challenging. In this paper we present a practical reasoning algorithm for the full DatalogMTL language, which we have implemented in a system called MeTeoR. Our approach effectively combines an optimised (but generally non-terminating) materialisation (a.k.a. forward chaining) procedure, which provides scalable behaviour, with an automata-based component that guarantees termination and completeness. To ensure favourable scalability of the materialisation component, we propose a novel seminaïve materialisation procedure for DatalogMTL enjoying the non-repetition property, which ensures that each rule instance will be applied at most once throughout its entire execution. Moreover, our materialisation procedure is enhanced with additional optimisations which further reduce the number of redundant computations performed during materialisation by disregarding rules as soon as it is certain that they cannot derive new facts in subsequent materialisation steps. Our extensive evaluation supports the practicality of our approach.