Abstract. We describe the conflict-driven answer set solver clasp, whichis based on concepts from constraint processing (CSP) and satisfiability checking (SAT). We detail its system architecture and major features, and provide a systematic empirical evaluation of its features. 1

via answer set programming

Abstract. Many real-world applications, like planning or model checking, comprise a parameter reflecting the size of a solution. In a propositional formalism like Answer Set Programming (ASP), such problems can only be dealt with in a bounded way, considering one problem instance after another by gradually increasing the bound on the solution size. We thus propose an incremental approach to both grounding and solving in ASP. Our goal is to avoid redundancy by gradually processing the extensions to a problem rather than repeatedly re-processing the entire (extended) problem. We start by furnishing a formal framework capturing our incremental approach in terms of module theory. In turn, we take advantage of this framework for guiding the successive treatment of program slices during grounding and solving. Finally, we describe the first integrated incremental ASP system, iclingo, and provide an experimental evaluation. 1

Abstract. We present a new approach to integrating Constraint Processing (CP) techniques into Answer Set Programming (ASP). Based on an alternative semantic approach, we develop an algorithmic framework for conflict-driven ASP solving that exploits CP solving capacities. A significant technical issue concerns the combination of conflict information from different solver types. We have implemented our approach, combining ASP solver clingo with the generic CP solver gecode, and we empirically investigate its computational impact. 1

We elaborate a uniform approach to computing answer sets of disjunctive logic programs based on state-of-theart Boolean constraint solving techniques. Starting from a constraint-based characterization of answer sets, we develop advanced solving algorithms, featuring backjumping and conflict-driven learning using the First-UIP scheme as well as sophisticated unfounded set checking. As a final result, we obtain a competitive solver for Σ P 2-complete problems, taking advantage of Boolean constraint solving technology without using any legacy solvers as black boxes.

We elaborate upon a recently proposed approach to finding an answer set of a logic program based on concepts from constraint processing and satisfiability checking. We extend this approach and propose a new algorithm for enumerating answer sets. The algorithm, which to our knowledge is novel even in the context of satisfiability checking, is implemented in the clasp answer set solver. We contrast our new approach to alternative systems and different options of clasp, and provide an empirical evaluation.

Identifying maternal and paternal inheritance is essential to be able to find the set of genes responsible for a particular disease. However, due to technological limitations, we have access to genotype data (genetic makeup of an individual), and determining haplotypes (genetic makeup of the parents) experimentally is a costly and time consuming procedure. With these biological motivations, we study a computational problem, called Haplotype Inference by Pure Parsimony (HIPP), that asks for the minimal number of haplotypes that form a given set of genotypes. HIPP has been studied using integer linear programming, branch and bound algorithms, SATbased algorithms, or pseudo-boolean optimization methods. We introduce a new approach to solving HIPP, using Answer Set Programming (ASP). According to our experiments with a large number of problem instances (some automatically generated and some real), our ASP-based approach solves the most number of problems compared with other approaches. Due to the expressivity of the knowledge representation language of ASP, our approach allows us to solve variations of HIPP, e.g., with additional domain specific information, such as patterns/parts of haplotypes observed for some gene family, or with some missing genotype information. In this sense, the ASP-based approach is more general than the existing approaches to haplotype inference.

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

Multi-Context Systems (MCS) are formalisms that enable the interlinkage of single knowledge bases, called contexts, via bridge rules. Recently, the evaluation of heterogeneous, nonmonotonic MCS was considered in Dao-Tran et al. (2010), where a fully distributed algorithm was described. In this paper, we continue this line of work and present a decomposition technique for MCS which analyzes the topology of an MCS. It applies pruning techniques to get economically small representations of context dependencies. Orthogonal to this, we characterize minimal interfaces for information exchange between contexts, such that data transmissions can be minimized. We then present a novel evaluation algorithm that operates on a query plan which is compiled with topology pruning and interface minimization. The effectiveness of the optimization techniques is demonstrated by a prototype implementation, which uses an off-the-shelf SAT solver and shows encouraging experimental results.