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

https://www.cambridge.org/core/journals/theory-and-practice-of-logic-programming/article/system-predictor-grounding-size-estimator-for-logic-programs-under-answer-set-semantics/9EE3D47F0DCDA77E39328E53B0816CD9#:~:text=System%20Predictor%3A%20Grounding%20Size%20Estimator%20for%20Logic%20Programs%20under%20Answer%20Set%20Semantics

Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system PREDICTOR (and its algorithmic backend) for estimating the grounding size of programs, a metric that can influence a performance of a system processing a program. We evaluate the impact of PREDICTOR when used as a guide for rewritings produced by the answer set programming rewriting tools PROJECTOR and LPOPT. The results demonstrate potential to this approach

An Experimental Model of Acute Humoral Rejection of Renal Allografts Associated with Concomitant Cellular Rejection

Acute humoral rejection (AHR), which occurs in up to 8% of kidney transplant recipients, is a significant cause of renal allograft dysfunction and loss. More efficacious treatment modalities are needed to eliminate or curtail alloantibody production and its deleterious effects on the kidney. The availability of animal models mimicking human AHR is essential to understand its pathophysiology and develop new treatment strategies. Using a mouse kidney transplant model, we demonstrate that presensitization of recipients with donor skin grafts results in rejection of subsequent renal allografts. All presensitized mice developed renal failure 8.6 ± 4.3 days after engraftment, with serum creatinine values near 100 μmol/dl. Graft histology revealed mild, diffuse, interstitial, mononuclear cell infiltrates; prominent peritubular capillary inflammatory cell margination; patchy interstitial hemorrhage; interstitial edema; and focal glomerular fibrin deposition. Complement (C3d) deposition was diffuse and prominent in peritubular capillaries. Serum analysis demonstrated high levels of circulating alloantibodies with broad cross-reactivity to many MHC haplotypes. The clinical setting and histological findings of our model strongly resemble AHR, which is frequently associated with cellular rejection, a situation commonly encountered in human renal allograft recipients. This animal model provides a valuable tool to study the pathogenesis of AHR, its relationship to cellular alloimmunity, its contribution to graft injury, and the effects of various potential therapeutic interventions