Prolog and ASP Inference Under One Roof

Answer set programming (ASP) is a declarative programming paradigm stemming from logic programming that has been successfully applied in various domains. Despite amazing advancements in ASP solving, many applications still pose a challenge that is commonly referred to as grounding bottleneck. Devising, implementing, and evaluating a method that alleviates this problem for certain application domains is the focus of this paper. The proposed method is based on combining backtracking-based search algorithms employed in answer set solvers with SLDNF resolution from PROLOG. Using PROLOG inference on non-ground portions of a given program, both grounding time and the size of the ground program can be substantially reduced

Speeding up Lazy-Grounding Answer Set Solving

The grounding bottleneck is an important open issue in Answer Set Programming. Lazy grounding addresses it by interleaving grounding and search. The performance of current lazy-grounding solvers is not yet comparable to that of ground-and-solve systems, however. The aim of this thesis is to extend prior work on lazy grounding by novel heuristics and other techniques like non-ground conflict learning in order to speed up solving. Parts of expected results will be beneficial for ground-and-solve systems as well

Grounding Size Predictions for Answer Set Programs

Answer set programming is a declarative programming paradigm geared towards solving difficult combinatorial search problems. Logic programs under answer set semantics can typically be written in many different ways while still encoding the same problem. These different versions of the program may result in diverse performances. Unfortunately, it is not always easy to identify which version of the program performs the best, requiring expert knowledge on both answer set processing and the problem domain. More so, the best version to use may even vary depending on the problem instance. One measure that has been shown to correlate with performance is the programs grounding size, a measure of the number of ground rules in the grounded program (Gebser et al. 2011). Computing a grounded program is an expensive task by itself, thus computing multiple ground programs to assess their sizes to distinguish between these programs is unrealistic. In this research, we present a new system called PREDICTOR to estimate the grounding size of programs without the need to actually ground/instantiate these rules. We utilize a simplified form of the grounding algorithms implemented by answer set programming grounder DLV while borrowing techniques from join-order size estimations in relational databases. The PREDICTOR system can be used independent of the chosen answer set programming grounder and solver system. We assess the accuracy of the predictions produced by PREDICTOR, while also evaluating its impact when used as a guide for rewritings produced by the automated answer set programming rewriting system called PROJECTOR. In particular, system PREDICTOR helps to boost the performance of PROJECTOR

Weighted-Sequence Problem: ASP vs CASP and Declarative vs Problem-Oriented Solving

Search problems with large variable domains pose a challenge to current answer-set programming (ASP) systems as large variable domains make grounding take a long time, and lead to large ground theories that may make solving infeasible. To circumvent the “grounding bottleneck” researchers proposed to integrate constraint solving techniques with ASP in an approach called constraint ASP (CASP). In the paper, we evaluate an ASP system CLINGO and a CASP system CLINGCON on a handcrafted problem involving large integer domains that is patterned after the database task of determining the optimal join order. We find that search methods used by CLINGO are superior to those used by CLINGCON, yet the latter system, not hampered by grounding, scales up better. The paper provides evidence that gains in solver technology can be obtained by further research on integrating ASP and CSP technologies