Roland Kaminski

Potassco: The Potsdam Answer Set Solving Collection

This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam.

Answer Set Solving in Practice

Answer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASPs solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples. Table of Contents: List of Figures / List of Tables / Motivation / Introduction / Basic modeling / Grounding / Characterizations / Solving / Systems / Advanced modeling / Conclusions

Engineering an Incremental ASP Solver

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.

Advances in gringo series 3

We describe the major new features emerging from a significant redesign of the grounder gringo, building upon a grounding algorithm based on semi-naive database evaluation. Unlike previous versions, rules only need to be safe rather than domain-restricted.

A portfolio solver for answer set programming: preliminary report

We propose a portfolio-based solving approach to Answer Set Programming (ASP). Our approach is homogeneous in considering several configurations of the ASP solver clasp. The selection among the configurations is realized via Support Vector Regression. The resulting portfolio-based solver claspfolio regularly outperforms clasps default configuration as well as manual tuning.

Reactive answer set programming

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.

Complex optimization in answer set programming

Preference handling and optimization are indispensable means for addressing nontrivial 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-prpogramming, 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.

On the Input Language of ASP Grounder Gringo

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.

Progress in clasp Series 3

We describe the novel functionalities comprised in clasp’s series 3. This includes parallel solving of disjunctive logic programs, parallel optimization with orthogonal strategies, declarative support for specifying domain heuristics, a portfolio of prefabricated expert configurations, and an application programming interface for library integration. This is complemented by experiments evaluating clasp 3’s optimization capacities as well as the impact of domain heuristics.

Multi-Criteria Optimization in Answer Set Programming

We elaborate upon new strategies and heuristics for solving multi-criteria optimization problems via Answer Set Programming (ASP). In particular, we conceive a new solving algorithm, based on conflict-driven learning, allowing for non-uniform descents during optimization. We apply these techniques to solve realistic Linux package configuration problems. To this end, we describe the Linux package configuration tool aspcud and compare its performance with systems pursuing alternative approaches.