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

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 present the first comprehensive approach to integrating cardinality and weight rules into conflict-driven ASP solving. We begin with a uniform, constraint-based characterization of answer sets in terms of nogoods. This provides the semantic underpinnings of our approach in fixing all necessary inferences that must be supported by an appropriate implementation. We then provide key algorithms detailing the salient features needed for implementing weight constraint rules. This involves a sophisticated unfounded set checker as well as an extended propagation algorithm along with the underlying data structures. We implemented our techniques within the ASP solver clasp and demonstrate their effectiveness by an experimental evaluation. 1

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Abstract. We extend to disjunctive logic programs our previous work on computing loop formulas of loops with at most one external support. We show that for these logic programs, loop formulas of loops with no external support can be computed in polynomial time, and if the given program has no constraints, an iterative procedure based on these formulas, the program completion, and unit propagation computes the least fixed point of a simplification operator used by DLV. We also relate loops with no external supports to the unfounded sets and the well-founded semantics of disjunctive logic programs by Wang and Zhou. However, the problem of computing loop formulas of loops with at most one external support rule is NP-hard for disjunctive logic programs. We thus propose a polynomial algorithm for computing some of these loop formulas, and show experimentally that this polynomial approximation algorithm can be effective in practice. 1

Abstract—Today’s molecular biology is confronted with enormous amounts of data, generated by new high-throughput technologies, along with an increasing number of biological models available over web repositories. This poses new challenges for bioinformatics to invent methods coping with incompleteness, heterogeneity, and mutual inconsistency of data and models. To this end, we built the library BioASP, providing a framework for analyzing biological data and models with Answer Set Programming (ASP). Due to the expressive modeling language, the inherent tolerance of incomplete knowledge, and efficient solving engines, ASP has proven to be an excellent tool for solving a variety of biological questions. The BioASP library implements methods for analyzing metabolic and gene regulatory networks, consistency checking, diagnosing, and repairing biological data and models. In particular, it allows for computing predictions and generating hypotheses about required expansions of biological models. To accomplish this, expert knowledge of both the biological application and the ASP paradigm needs to be combined. In fact, the functionalities provided by the BioASP library exploit technical know-how of modeling (biological) problems in ASP and gearing ASP solvers ’ parameters to them. Often, such bestpractice technology is the result of an exhaustive series of tests. The BioASP library integrates our practical experience and offers them via easy-to-use Python functions, thus enabling ASP nonexperts to solve biological questions with ASP. I.

We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to reestablish their mutual consistency—an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.

Abstract. We introduce an interactive procedural-style debugging approach for answer-set programs that avoids the negative aspects of non-declarative debugging of ASP. It is based on an intuitive computation model that allows a user to follow his or her intuition by stepwise determining which applicable rules are considered to be supporting rules. Moreover, we define the notion of a breakpoint for answer-set programs that allows to start stepwise debugging of a program Π from an answer set of a subprogram of Π. 1

submitted [TBA]; revised [TBA]; accepted [TBA] Preference handling and optimization are indispensable means for addressing non-trivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, like saturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-programming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications. 1

Abstract argumentation frameworks (AFs) provide the basis for various reasoning problems in the areas of Knowledge Representation and Artificial Intelligence. Efficient evaluation of AFs has thus been identified as an important research challenge. So far, implemented systems for evaluating AFs have either followed a straight-forward reduction-based approach or been limited to certain tractable classes of AFs. In this work, we present a generic approach for reasoning over AFs, based on the novel concept of complexity-sensitivity. Establishing the theoretical foundations of this approach, we derive several new complexity results for preferred, semistable and stage semantics which complement the current complexity landscape for abstract argumentation, providing further understanding on the sources of intractability of AF reasoning problems. The introduced generic framework exploits decision procedures for problems of lower complexity whenever possible. This allows, in particular, instantiations of the generic framework via harnessing in an iterative way current sophisticated Boolean satisfiability (SAT) solver technology for solving the considered AF reasoning problems. First experimental results show that the SAT-based instantiation of our novel approach outperforms existing systems.