Abstract. We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming. To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on Answer Set Programming to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies in the data by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions. 1

We introduce a knowledge representation language AC(C) extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, ACsolver, for computing answer sets of AC(C) programs. The algorithm does not require full grounding of a program and combines “classical” ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The AC(C) based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We belief that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm. 1

Abstract. Many real-world problems require the enumeration of all solutions of combinatorial search problems, even though this is often infeasible in practice. However, not always all parts of a solution are needed. We are thus interested in projecting solutions to a restricted vocabulary. Yet, the adaption of Boolean constraint solving algorithms turns out to be non-obvious provided one wants a repetition-free enumeration in polynomial space. We address this problem and propose a new algorithm computing projective solutions. Although we have implemented our approach in the context of Answer Set Programming, it is readily applicable to any solver based on modern Boolean constraint technology. 1

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 propose an approach to distributed Answer Set Solving based on Message Passing. Our approach aims at taking advantage of modern ASP solvers rather than proposing a genuine yet involved parallel ASP solver. To this end, we rely upon a simple master-worker architecture in which each worker amounts to an off-the-shelf ASP solver augmented with a separate communication module being only lightly connected to the actual solver. The overall communication is driven by the workers ’ communication modules, which asynchronously exchange messages with the master. We have implemented our approach and report upon an empirical study demonstrating its computational impact. 1

Abstract. This paper presents the concept of parallelisation of a solver for Answer Set Programming (ASP). While there already exist some approaches to parallel ASP solving, there was a lack of a parallel version of the powerful clasp solver. We implemented a parallel version of clasp based on message-passing. Experimental results on Blue Gene P/L indicate the potential of such an approach.

Abstract. We report on three recent advances in the distributed ASP solver claspar. First, we describe its flexible architecture supporting various search strategies, including competitive search using a portfolio of solver configurations. Second, we describe claspar’s distributed learning capacities that allow for sharing learned nogoods among solver instances. Finally, we discuss claspar’s approach to distributed optimization. 1