Towards Observing the Effect of Abstraction on Understandability of Explanations in Answer Set Programming

Abstract

In order for AI systems to provide explanations of their decision-making that are concise and understandable, they need to have the ability of getting rid of irrelevant details and presenting a higher-level view. Answer Set Programming (ASP) is one of the core formalisms of Symbolic AI, based on logic programming with stable model semantics, widely used in various applications. Explaining the solutions (i.e., answer sets) of an answer set program continues to be a widely studied topic, with various systems available. This technical communication reports on a preliminary study for observing the effect of abstraction on the understandability of ASP explanations, by considering the recent abstraction notions which preserve the dependencies as much as possible while abstracting over answer set programs. We describe our experiment design which will be our base for further extensions of the study. Our preliminary results show the challenge of capturing the effect of abstraction on understandability, requiring further investigations in this direction.