Abstract. We summarize the salient features of the current version of the answer set solver clasp, focusing on the progress made since version RC4 of clasp. Apart from enhanced preprocessing and search-supporting techniques, a particular emphasis lies on advanced reasoning modes, such as cautious and brave reasoning, optimization, solution projection, and incremental solving. 1

Abstract. We report on recent advancements in the development of grounder Gringo for logic programs under answer set semantics. Like its relatives, DLV and Lparse, Gringo has in the meantime reached maturity and offers a rich modeling language to program developers. The attractiveness of Gringo is fostered by the fact that it significantly extends the input language of Lparse while supporting a compatible output format, recognized by many state-of-the-art ASP solvers. 1

Abstract. We address the problem of Finite Model Computation (FMC) of firstorder theories and show that FMC can efficiently and transparently be solved by taking advantage of a recent extension of Answer Set Programming (ASP), called incremental Answer Set Programming (iASP). The idea is to use the incremental parameter in iASP programs to account for the domain size of a model. The FMC problem is then successively addressed for increasing domain sizes until an answer set, representing a finite model of the original first-order theory, is found. We implemented a system based on the iASP solver iClingo and demonstrate its competitiveness by showing that it slightly outperforms the winner of the FNT division of CADE’s Automated Theorem Proving (ATP) competition. 1

Abstract. We propose a qualitative approach to elaborating the biosynthetic capacities of metabolic networks. In fact, large-scale metabolic networks as well as measured datasets suffer from substantial incompleteness. Moreover, traditional formal approaches to biosynthesis require kinetic information, which is rarely available. Our approach builds upon a formal method for analyzing largescale metabolic networks. Mapping its principles into Answer Set Programming (ASP) allows us to address various biologically relevant problems. In particular, our approach benefits from the intrinsic incompleteness-tolerating capacities of ASP. Our approach is indorsed by recent complexity results, showing that the reconstruction of metabolic networks and related problems are NP-hard. 1

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and lacking information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-theart localization and tracking methods can be combined with answer set programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation. 1

Abstract. This paper presents our program in B-Prolog submitted to the third ASP solver competition for the Sokoban problem. This program, based on dynamic programming, treats Sokoban as a generalized shortest path problem. It divides a problem into independent subproblems and uses mode-directed tabling to store subproblems and their answers. This program is very simple but quite efficient. Without use of any sophisticated domain knowledge, it easily solved 11 of the 15 instances used in the competition. 1

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RDF/S ontologies are often used in e-science to express domain knowledge regarding the respective field of investigation (e.g., cultural informatics, bioinformatics etc). Such ontologies need to change often to reflect the latest scientific understanding on the domain at hand, and are usually associated with constraints expressed using various declarative formalisms to express domain-specific requirements, such as cardinality or acyclicity constraints. Addressing the evolution of ontologies in the presence of ontological constraints imposes extra difficulties, because it forces us to respect the associated constraints during evolution. While these issues were addressed in previous work, this is the first work to examine how ASP techniques can be applied to model and implement the evolution process. ASP was chosen for its advantages in terms of a principled, rather than ad hoc implementation, its modularity and flexibility, and for being a state-of-the-art technique to tackle hard combinatorial problems. In particular, our approach consists in providing a general translation of the problem into ASP, thereby reducing it to an instance of an ASP program that can be solved by an ASP solver. Our experiments are promising, even for large ontologies, and also show that the scalability of the approach depends on the morphology of the input.

Abstract. We introduce the first approach to Reactive Answer Set Programming, aiming at reasoning about real-time dynamic systems running online in changing environments. We start by laying the theoretical foundations by appeal to module theory. With this, we elaborate upon the composition of the various offline and online programs in order to pave the way for stream-driven grounding and solving. Finally, we describe the implementation of a reactive ASP solver, oclingo. 1