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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Model-Theoretic View on Qualitative Constraint Reasoning
Authors: Manuel Bodirsky, Peter Jonsson
JAIR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this survey we present a model-theoretic perspective on qualitative constraint reasoning and explain some of the basic concepts and results in an accessible way. In particular, we discuss the significance of ω-categoricity for qualitative reasoning, of primitive positive interpretations for complexity analysis, and of Datalog as a unifying language for describing local consistency algorithms. |
| Researcher Affiliation | Academia | Manuel Bodirsky EMAIL Institut für Algebra TU Dresden 01062 Dresden, Germany Peter Jonsson EMAIL Department of Computer Science Linköping University SE-581 83 Linköping, Sweden |
| Pseudocode | Yes | PCA(N) Input: an A-network N = (V, f). Do For all distinct nodes x, y, z V : Replace f(x, y) by f(x, y) (f(x, z) f(z, y)) If f(x, y) = 0 then reject Loop until no further changes Return (V, f). |
| Open Source Code | No | The paper is a theoretical survey and review of existing concepts and results. It does not propose a new methodology or system that would have associated code. Therefore, no open-source code is provided or mentioned. |
| Open Datasets | No | The paper is a theoretical survey and does not involve experimental evaluation using datasets. It discusses formalisms like Allen's Interval Algebra and RCC-5 as theoretical examples, not as empirical datasets for experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or dataset evaluation. Therefore, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on model-theoretic concepts. It does not describe any experiments or their computational execution. Consequently, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical survey and does not involve software implementation or execution. Therefore, no specific software dependencies or version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical, presenting a model-theoretic view on qualitative constraint reasoning. It does not involve any experimental setup, hyperparameters, or training configurations. |