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].
A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation
Authors: Jeroen Spaans, Jesse Heyninck
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate an extension of CLP which unifies many of these extensions and allows negation in the body. We provide semantics for such programs, using the framework of approximation fixpoint theory, and give a detailed overview of the impacts of properties of the semirings on the resulting semantics. As such, we provide a unifying framework that captures existing approaches and allows extending them with a more expressive language. In more detail, the contributions of this paper are the following: (1) we unify the approaches of [Bistarelli et al., 2001; Khamis et al., 2023] in the framework of AFT, making clear the different assumptions used in these approaches and how they affect the behaviour of the corresponding semantics, (2) we generalize the language of semiring-based constraint logic programming to allow for negation in the body of rules, and (3) use AFT to define and study semantics for such programs, introducing among others the stable and well-founded semantics, and show how they generalize both existing approaches to (positive) SCLPs and normal logic programs (nlps). |
| Researcher Affiliation | Academia | 1Open Universiteit, The Netherlands 2University of Cape Town, South Africa EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Procedures are described using mathematical definitions, lemmas, and theorems. |
| Open Source Code | No | Future work includes computational complexity, implementations, and applying AFT-based notions such as stratification [Vennekens et al., 2004], conditional independence [Heyninck, 2024] and non-determinism [Heyninck et al., 2024]. |
| Open Datasets | No | The paper uses illustrative examples (e.g., Example 1: 'We consider the following program P ... as a running example.') to demonstrate its theoretical framework and semantics. It does not mention any publicly available or open datasets used for empirical evaluation, nor does it provide access information for any data. |
| Dataset Splits | No | The paper describes a theoretical framework and does not involve empirical experiments with datasets. Therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper focuses on a theoretical framework and does not describe any computational experiments. Consequently, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper introduces a theoretical framework for semiring-based constraint logic programming. It does not provide details on specific software, libraries, or their version numbers, as it does not describe an implementation or empirical experiments. |
| Experiment Setup | No | The paper is theoretical, presenting a unifying framework and its semantics. It does not include any experimental setup details, hyperparameters, or training configurations, as no empirical experiments are described. |