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We present the hybrid ASP solver clingcon, combining the simple modeling language and the high performance Boolean solving capacities of Answer Set Programming (ASP) with techniques for using non-Boolean constraints from the area of Constraint Programming (CP). The new clingcon system features an extended syntax supporting global constraints and optimize statements for constraint variables. The major technical innovation improves the interaction between ASP and CP solver through elaborated learning techniques based on irreducible inconsistent sets. A broad empirical evaluation shows that these techniques yield a performance improvement of an order of magnitude.

The recent series 5 of the Answer Set Programming (ASP) systemclingoprovides generic means to enhance basic ASP with theory reasoning capabilities. We instantiate this framework with different forms of linear constraints and elaborate upon its formal properties. Given this, we discuss the respective implementations, and present techniques for using these constraints in a reactive context. More precisely, we introduce extensions toclingowith difference and linear constraints over integers and reals, respectively, and realize them in complementary ways. Finally, we empirically evaluate the resultingclingoderivativesclingo[dl] andclingo[lp] on common language fragments and contrast them to related ASP systems.

Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models.

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We present the third generation of the constraint answer set system clingcon, combining Answer Set Programming (ASP) with finite domain constraint processing (CP). While its predecessors rely on a black-box approach to hybrid solving by integrating the CP solver gecode, the new clingcon system pursues a lazy approach using dedicated constraint propagators to extend propagation in the underlying ASP solver clasp. No extension is needed for parsing and grounding clingcon's hybrid modeling language since both can be accommodated by the new generic theory handling capabilities of the ASP grounder gringo. As a whole, clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies on the grounder gringo and the solver clasp. The new approach of clingcon offers a seamless integration of CP propagation into ASP solving that benefits from the whole spectrum of clasp's reasoning modes, including for instance multi-shot solving and advanced optimization techniques. This is accomplished by a lazy approach that unfolds the representation of constraints and adds it to that of the logic program only when needed. Although the unfolding is usually dictated by the constraint propagators during solving, it can already be partially (or even totally) done during preprocessing. Moreover, clingcon's constraint preprocessing and propagation incorporate several well established CP techniques that greatly improve its performance. We demonstrate this via an extensive empirical evaluation contrasting, first, the various techniques in the context of CSP solving and, second, the new clingcon system with other hybrid ASP systems.A black-box approach is pursued in the two previous clingcon series where the ASP solver clasp is combined with the CP solver gecode (Gecode Team 2006) by following the lazy approach to SMT 3 solving (Barrett et al. 2009). In the clingcon setting, this means that clasp only generates truth assignments for abstracted constraint expressions, while gecode checks whether the actual constraints can be made true or false accordingly. On the one hand, this black-box approach benefits from the vast spectrum of constraints available in gecode and seamlessly keeps up with advanced CP technology, among others regarding preprocessing and propagation. Moreover, this approach avoids an explicit representation of integer variables in ASP and thus can deal with very large domains. On the other hand, the usage of an external CP solver restricts information exchange which impedes the CDCL approach of clasp. First, neither conflict nor propagation information is provided by gecode and thus must be approximated within the interface to sustain conflict analysis in CDCL. Second, the granularity induced by constraint abstraction leads to weaker propagation than what is obtainable when encoding integer variables.A translation-based approach is pursued by the aspartame system (Banbara et al . 2015) where a CSP 4 is fully translated into ASP and then solved by an ASP solver. This approach follows the one of the CP solver sugar (...