AILOJul 14, 2021

Forgetting in Answer Set Programming -- A Survey

arXiv:2107.07016v312 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental survey that addresses the lack of a unified overview of forgetting operators for researchers and practitioners in Answer Set Programming.

The paper surveys forgetting (variable elimination) operators in Answer Set Programming, examining existing properties and operators to provide a comprehensive overview and guide users in selecting appropriate operators for their applications.

Forgetting - or variable elimination - is an operation that allows the removal, from a knowledge base, of middle variables no longer deemed relevant. In recent years, many different approaches for forgetting in Answer Set Programming have been proposed, in the form of specific operators, or classes of such operators, commonly following different principles and obeying different properties. Each such approach was developed to address some particular view on forgetting, aimed at obeying a specific set of properties deemed desirable in such view, but a comprehensive and uniform overview of all the existing operators and properties is missing. In this paper, we thoroughly examine existing properties and (classes of) operators for forgetting in Answer Set Programming, drawing a complete picture of the landscape of these classes of forgetting operators, which includes many novel results on relations between properties and operators, including considerations on concrete operators to compute results of forgetting and computational complexity. Our goal is to provide guidance to help users in choosing the operator most adequate for their application requirements.

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