Michael Morak

On the complexity of ontological reasoning under disjunctive existential rules

Ontology-based data access is an emerging yet powerful technology that allows to enhance a classical relational database with an ontology in order to infer new intensional knowledge. Recently, Datalog+/- was introduced with the purpose of providing tractable reasoning algorithms for expressive ontology languages. In this framework, Datalog is extended by features such as existential quantification in rule heads, and at the same time the rule syntax is restricted to guarantee decidability, and also tractability, of relevant reasoning tasks. In this paper, we enrich Datalog even more by allowing not only existential quantification but also disjunction in rule heads, and we investigate the complexity of reasoning under the obtained formalism.

dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation

The aim of this paper is to announce the release of a novel system for abstract argumentation which is based on decomposition and dynamic programming. We provide first experimental evaluations to show the feasibility of this approach.

D-flat: Declarative problem solving using tree decompositions and answer-set programming

In this work, we propose Answer-Set Programming (ASP) as a tool for rapid prototyping of dynamic programming algorithms based on tree decompositions. In fact, many such algorithms have been designed, but only a few of them found their way into implementation. The main obstacle is the lack of easy-to-use systems which (i) take care of building a tree decomposition and (ii) provide an interface for declarative specifications of dynamic programming algorithms. In this paper, we present D-FLAT, a novel tool that relieves the user of having to handle all the technical details concerned with parsing, tree decomposition, the handling of data structures, etc. Instead, it is only the dynamic programming algorithm itself which has to be specified in the ASP language. D-FLAT employs an ASP solver in order to compute the local solutions in the dynamic programming algorithm. In the paper, we give a few examples illustrating the use of D-FLAT and describe the main features of the system. Moreover, we report experiments which show that ASP-based D-FLAT encodings for some problems outperform monolithic ASP encodings on instances of small treewidth.

Answer Set Solving with Bounded Treewidth Revisited

Parameterized algorithms are a way to solve hard problems more efficiently, given that a specific parameter of the input is small. In this paper, we apply this idea to the field of answer set programming (ASP). To this end, we propose two kinds of graph representations of programs to exploit their treewidth as a parameter. Treewidth roughly measures to which extent the internal structure of a program resembles a tree. Our main contribution is the design of parameterized dynamic programming algorithms, which run in linear time if the treewidth and weights of the given program are bounded. Compared to previous work, our algorithms handle the full syntax of ASP. Finally, we report on an empirical evaluation that shows good runtime behaviour for benchmark instances of low treewidth, especially for counting answer sets.

Evaluating Tree-Decomposition Based Algorithms for Answer Set Programming

A promising approach to tackle intractable problems is given by a combination of decomposition methods with dynamic algorithms. One such decomposition concept is tree decomposition. However, several heuristics for obtaining a tree decomposition exist and, moreover, also the subsequent dynamic algorithm can be laid out differently. In this paper, we provide an experimental evaluation of this combined approach when applied to reasoning problems in propositional answer set programming. More specifically, we analyze the performance of three different heuristics and two different dynamic algorithms, an existing standard version and a recently proposed algorithm based on a more involved data structure, but which provides better theoretical runtime. The results suggest that a suitable combination of the tree decomposition heuristics and the dynamic algorithm has to be chosen carefully. In particular, we observed that the performance of the dynamic algorithm highly depends on certain features (besides treewidth) of the provided tree decomposition. Based on this observation we apply supervised machine learning techniques to automatically select the dynamic algorithm depending on the features of the input tree decomposition.

Guarded-Based Disjunctive Tuple-Generating Dependencies

We perform an in-depth complexity analysis of query answering under guarded-based classes of disjunctive tuple-generating dependencies (DTGDs), focusing on (unions of) conjunctive queries ((U)CQs). We show that the problem under investigation is very hard, namely 2ExpTime-complete, even for fixed sets of dependencies of a very restricted form. This is a surprising lower bound that demonstrates the enormous impact of disjunction on query answering under guarded-based tuple-generating dependencies, and also reveals the source of complexity for expressive logics such as the guarded fragment of first-order logic. We then proceed to investigate whether prominent subclasses of (U)CQs (i.e., queries of bounded treewidth and hypertree-width, and acyclic queries) have a positive impact on the complexity of the problem under consideration. We show that queries of bounded treewidth and bounded hypertree-width do not reduce the complexity of our problem, even if we focus on predicates of bounded arity or on fixed sets of DTGDs. Regarding acyclic queries, although the problem remains 2ExpTime-complete in general, in some relevant settings the complexity reduces to ExpTime-complete. Finally, with the aim of identifying tractable cases, we focus our attention on atomic queries. We show that atomic queries do not make the query answering problem easier under classes of guarded-based DTGDs that allow more than one atom to occur in the body of the dependencies. However, the complexity significantly decreases in the case of dependencies that can have only one atom in the body. In particular, we obtain a Ptime-completeness if we focus on predicates of bounded arity, and AC0-membership when the set of dependencies and the query are fixed. Interestingly, our results can be used as a generic tool for establishing complexity results for query answering under various description logics.

Preprocessing of Complex Non-Ground Rules in Answer Set Programming

In this paper we present a novel method for preprocessing complex non-ground rules in answer set programming (ASP). Using a well-known result from the area of conjunctive query evaluation, we apply hypertree decomposition to ASP rules in order to make the structure of rules more explicit to grounders. In particular, the decomposition of rules reduces the number of variables per rule, while on the other hand, additional predicates are required to link the decomposed rules together. As we show in this paper, this technique can reduce the size of the grounding significantly and thus improves the performance of ASP systems in certain cases. Using a prototype implementation and the benchmark suites of the Answer Set Programming Competition 2011, we perform extensive tests of our decomposition approach that clearly show the improvements in grounding time and size.

A dynamic-programming based ASP-solver

We present a novel system for propositional Answer-Set Programming (ASP). This system, called dynASP, is based on dynamic programming and thus significantly differs from standard ASP-solvers which implement techniques stemming from SAT or CSP.

DynASP2.5: Dynamic Programming on Tree Decompositions in Action

A vibrant theoretical research area are efficient exact parameterized algorithms. Very recent solving competitions such as the PACE challenge show that there is also increasing practical interest in the parameterized algorithms community. An important research question is whether dedicated parameterized exact algorithms exhibit certain practical relevance and one can even beat well-established problem solvers. We consider the logic-based declarative modeling language and problem solving framework Answer Set Programming (ASP). State-of-the-art ASP solvers rely considerably on Sat-based algorithms. An ASP solver (DynASP2), which is based on a classical dynamic programming on tree decompositions, has been published very recently. Unfortunately, DynASP2 can outperform modern ASP solvers on programs of small treewidth only if the question of interest is to count the number of solutions. In this paper, we describe underlying concepts of our new implementation (DynASP2.5) that shows competitive behavior to state-of-the-art ASP solvers even for finding just one solution when solving problems as the Steiner tree problem that have been modeled in ASP on graphs with low treewidth. Our implementation is based on a novel approach that we call multi-pass dynamic programming (M-DPSINC).

The Impact of Treewidth on ASP Grounding and Solving

In this paper, we aim to study how the performance of modern answer set programming (ASP) solvers is influenced by the treewidth of the input program and to investigate the consequences of this relationship. We first perform an experimental evaluation that shows that the solving performance is heavily influenced by the treewidth, given ground input programs that are otherwise uniform, both in size and construction. This observation leads to an important question for ASP, namely, how to design encodings such that the treewidth of the resulting ground program remains small. To this end, we define the class of connection-guarded programs, which guarantees that the treewidth of the program after grounding only depends on the treewidth (and the degree) of the input instance. In order to obtain this result, we formalize the grounding process using MSO transductions.