Explanations for Answer Set Programming
This work addresses the need for better interpretability in ASP for users in AI and logic programming, though it is incremental as it builds on an existing system.
The paper tackles the problem of generating explanations for Answer Set Programming (ASP) by enhancing the xASP system to support more clingo constructs like choice rules and aggregates, resulting in xASP2, which formalizes an explainable AI system for a broad ASP fragment and minimizes assumptions while presenting explanations as directed acyclic graphs.
The paper presents an enhancement of xASP, a system that generates explanation graphs for Answer Set Programming (ASP). Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the aggregates such as #sum, #min. This work formalizes and presents an explainable artificial intelligence system for a broad fragment of ASP, capable of shrinking as much as possible the set of assumptions and presenting explanations in terms of directed acyclic graphs.