6 research outputs found
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
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Systems, Engineering Environments, and Competitions
The goal of this paper is threefold. First, we trace the history of the development of answer set solvers, by accounting for more than a dozen of them. Second, we discuss development tools and environments that facilitate the use of answer set programming technology in practical applications. Last, we present the evolution of the answer set programming competitions, prime venues for tracking advances in answer set solving technology