Rough set reasoning using answer set programs

Reasoning about uncertainty is one of the main cornerstones Knowledge Representation. Formal representations are numerous and highly varied due to different types intended be modeled such as vagueness, imprecision incompleteness. There a rich body theoretical results that has been generated for many these approaches. It often case though, pragmatic tools reasoning with lag behind this results. Rough set theory approach modeling incompleteness based on indiscernibility its generalizations. In paper, we provide tool constructively generalized rough approximations use Answer Set Programming (Asp). We an interpretation answer sets (generalized) crisp (when possible) show how Asp solvers situated in realistic knowledge bases. The paper includes generic templates doing also provides study showing techniques can used generate reducts incomplete information systems. Complete, ready run clingo code provided Appendix, all programs considered. These executed validation purposes solver.