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.
The paper describes an ongoing effort in developing a declarative system for supporting operators in the Nuclear Power Plant (NPP) control room. The focus is on two modules: diagnosis and explanation of events that happened in NPPs. We describe an Answer Set Programming (ASP) representation of an NPP, which consists of declarations of state variables, components, their connections, and rules encoding the plant behavior. We then show how the ASP program can be used to explain the series of events that occurred in the Three Mile Island, Unit 2 (TMI-2) NPP accident, the most severe accident in the USA nuclear power plant operating history. We also describe an explanation module aimed at addressing answers to questions such as “why an event occurs?” or “what should be done?” given the collected data.
We present an enhancement of exp(ASP), a system that generates explanation graphs for a literal -an atom a or its default negation ∼ a-given an answer set A of a normal logic program P, which explain why is true (or false) given A and P. The new system, exp(ASP c ), differs from exp(ASP) in that it supports choice rules and utilizes constraint rules to provide explanation graphs that include information about choices and constraints.
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