Computing Supported Models via Transformation to Stable Models
Fang Li, Gopal Gupta

TL;DR
This paper introduces a transformation method that enables the computation of supported models within standard ASP frameworks, expanding the reasoning capabilities beyond traditional stable models.
Contribution
It presents the first practical transformation technique and implementation for computing supported models using modern ASP solvers like Clingo.
Findings
Supports applications in verification, diagnosis, and planning.
Empirical evaluation shows practical utility and efficiency.
Provides a publicly available tool compatible with standard ASP syntax.
Abstract
Answer Set Programming (ASP) with stable model semantics has proven highly effective for knowledge representation and reasoning. However, the minimality requirement of stable models can be restrictive for applications requiring exploration of non-minimal but logically consistent solution spaces. Supported models, introduced by Apt, Blair, and Walker in 1988, relax this minimality constraint while maintaining a support condition ensuring every true atom is justified by some rule. Despite their theoretical significance, supported models lack practical computational tools integrated with modern ASP solvers. We present a novel transformation-based method enabling computation of supported models using standard ASP infrastructure. Our approach transforms any ground logic program into an equivalent program whose stable models correspond exactly to the supported models of the original…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Multi-Agent Systems and Negotiation · Semantic Web and Ontologies
