Applying Answer Set Programming with Fuzzy Membership Functions: a Case Study
For researchers in knowledge representation and reasoning, this work offers a principled framework to handle vagueness in ASP, but it is incremental as it builds on existing fuzzy logic and ASP concepts without providing quantitative results.
This paper presents a fuzzy-logic-based qualitative extension of Answer Set Programming (ASP) that bridges numerical information and qualitative reasoning, demonstrating its ability to integrate machine learning outputs with symbolic reasoning over qualitative labels in a representative use case.
Human reasoning often operates through qualitative concepts expressed by linguistic labels such as high, low, expensive, or cheap, whose interpretation depends on context and is usually vague, despite being rooted in numerical data. This paper explores a novel fuzzy-logic-based qualitative extension of Answer Set Programming (ASP) to bridge numerical information and qualitative reasoning. The underlying language, formally introduced in a separate work, provides a principled framework that avoids rigid thresholds and supports robust reasoning under vagueness. Focusing on a representative use case, we illustrate how the framework integrates numerically grounded inputs (such as outputs of machine learning models) with symbolic reasoning over qualitative labels. Key features, including learning-based membership functions and semantically enriched predicates, enable the combination of expert knowledge, contextual factors, and subjective interpretations within a unified declarative setting.