Multi-engine ASP solving with policy adaptation

The recent application of Machine Learning techniques to the Answer Set Programming (ASP) field proved to be effective. In particular, the multi-engine ASP solverME-ASPis efficient: it is able to solve more instances than any other ASP system that participated to the 3rd ASP Competition on the "System Track" benchmarks. In theME-ASPapproach, classification methods inductively learn off-line algorithm selection policies starting from both a set offeaturesof instances in atrainingset, and the solvers performance on such instances.In this paper we present an improvement to the multi-engine framework ofME-ASP, in which we add the capability of updating the learned policies when the original approach fails to give good predictions. An experimental analysis, conducted on training and test sets of ground instances obtained from the ones submitted to the "System Track" of the 3rd ASP Competition, shows that the policy adaptation improves the performance ofME-ASPwhen applied to test sets containing domains of instances that were not considered for training.