Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].

Non-expansive Fuzzy ALC

Authors: Stefan Gebhart, Lutz Schrรถder, Paul Wild

IJCAI 2025 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We present an unlabelled tableau method for non-expansive fuzzy ALC, which allows reasoning over general TBoxes in EXPTIME like in two-valued ALC. Our main technical result on non-expansive fuzzy ALC is decidability of the main reasoning problems in the same complexity as for two-valued ALC; most notably, threshold satisfiability over general TBoxes is (only) EXPTIME-complete, in sharp contrast with the undecidability encountered for full ukasiewicz fuzzy ALC, and in spite of the fact that the semantics of general concept inclusions is pointwise inequality and thus corresponds to validity of ukasiewicz implication. We base this result on an unlabelled tableau calculus.
Researcher Affiliation Academia Stefan Gebhart , Lutz Schr oder and Paul Wild Friedrich-Alexander-Universit at Erlangen-N urnberg EMAIL
Pseudocode Yes Algorithm 1: checking satisfiability in non-expansive fuzzy ALC
Open Source Code No No explicit statement or link regarding open-source code for the methodology described in this paper was found.
Open Datasets No The paper focuses on theoretical aspects of fuzzy description logics, introducing a new logic and a tableau calculus, and does not mention the use or availability of any datasets.
Dataset Splits No As the paper is theoretical and does not describe experiments using datasets, there is no mention of dataset splits.
Hardware Specification No The paper is theoretical and does not describe any experiments or specific hardware used for computations.
Software Dependencies No The paper describes a theoretical framework and an algorithm but does not specify any software dependencies with version numbers.
Experiment Setup No The paper is theoretical and focuses on logic and algorithm design, thus it does not include details on experimental setup or hyperparameters.