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 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. |