Author name cluster

Isabelle Kuhlmann

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6 papers

2 author rows

JAIR Journal 2025 Journal Article

Comparison of SAT-Based and ASP-Based Algorithms for Inconsistency Measurement

Isabelle Kuhlmann
Anna Gessler
Vivien Laszlo
Matthias Thimm

We present algorithms based on satisfiability problem (SAT) solving, as well as answer set programming (ASP), for solving the problem of determining inconsistency degrees in propositional knowledge bases. We consider six different inconsistency measures whose respective decision problems lie on the first level of the polynomial hierarchy. Namely, these are the contension, forgetting-based, hitting set, max-distance, sum-distance, and hit-distance inconsistency measures. In an extensive experimental analysis, we compare the SAT-based and ASP-based approaches with each other, as well as with a set of naive baseline algorithms. Our results demonstrate that, overall, both the SAT-based and the ASP-based approaches clearly outperform the naive baseline methods in terms of runtime. The results further show that the proposed ASP-based approaches perform superior to the SAT-based ones with regard to all six inconsistency measures considered in this work. Moreover, we conduct additional experiments to explain the aforementioned results in greater detail.

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KR Conference 2025 System Paper

Exploring Desirable Configurations in Global Logistics with Heuristic Search in Answer Set Programming

Olcay Altay-Kern
Emmanuelle Dietz
Isabelle Kuhlmann
Matthias Thimm

In the design of global logistics problems, the solution spaces are typically extremely large. To demonstrate how these challenges can be addressed in Answer Set Programming (ASP), this work investigates a representative industrial use case of a global logistics problem in the aerospace problem domain. An exploration of specific areas of the search space is done by using heuristic-driven solving for the formulation of domain heuristics that guide the solver to potentially desirable configurations. A quantitative evaluation on the Key Performance Indicators and a qualitative evaluation on the variability of the models by means of a similarity analysis shows promising results.

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NMR Workshop 2023 Conference Paper

An ASP-Based Framework for Solving Problems Related to Declarative Process Specifications

Isabelle Kuhlmann
Carl Corea
John Grant

We present a framework of answer set programming-based solutions for various problems related to declarative process specifications. Specifically, the framework offers implementations for conformance checking, satisfiability checking, and two different inconsistency measures. Since the aforementioned problems are represented in a fragment of linear temporal logic, the framework could also prove useful for a broader range of applications beyond process specifications.

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JELIA Conference 2023 Conference Paper

Computing MUS-Based Inconsistency Measures

Isabelle Kuhlmann
Andreas Niskanen
Matti Järvisalo

Abstract We detail two instantiations of a generic algorithm for the problematic and MUS-variable-based inconsistency measures, based on answer set programming and Boolean satisfiability (SAT). Empirically, the SAT-based approach allows for more efficiently computing the measures when compared to enumerating all minimal correction subsets of a knowledge base.

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ECAI Conference 2023 Conference Paper

MaxSAT-Based Inconsistency Measurement

Andreas Niskanen
Isabelle Kuhlmann
Matthias Thimm
Matti Järvisalo

Inconsistency measurement aims at obtaining a quantitative assessment of the level of inconsistency in knowledge bases. While having such a quantitative assessment is beneficial in various settings, inconsistency measurement of propositional knowledge bases is under most existing measures a significantly challenging computational task. In this work, we harness Boolean satisfiability (SAT) based solving techniques for developing practical inconsistency measurement algorithms. Our algorithms—some of which constitute, to the best of our knowledge, the first practical approaches for specific inconsistency measures—are based on using natural choices of SAT-based techniques for the individual inconsistency measures, ranging from direct maximum satisfiability (MaxSAT) encodings to MaxSAT-based column generation techniques making use of incremental computations. We show through an extensive empirical evaluation that our approaches scale well in practice and significantly outperform recently-proposed answer set programming approaches to inconsistency measurement.

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KR Conference 2021 Short Paper

Distinguishability in Abstract Argumentation

Isabelle Kuhlmann
Tjitze Rienstra
Lars Bengel
Kenneth Skiba
Matthias Thimm

In abstract argumentation, the admissible semantics can be said to distinguish the preferred semantics in the sense that argumentation frameworks with the same admissible extensions also have the same preferred extensions. In this paper we present an exhaustive study of such distinguishability relationships, including those between sets of semantics. We further examine restricted classes of argumentation frameworks, such as self-attack-free and acyclic frameworks. We discuss the relevance of our results in the context of the argumentation framework elicitation problem.

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