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].
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers
Authors: Ondrej Kuzelka
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we study weighted first-order model counting (WFOMC), which is an important problem (not only) because it can be used for probabilistic inference in most statistical relational learning models (Van den Broeck et al., 2011; Getoor & Taskar, 2007). ... Motivated by the work of Kuusisto and Lutz, in this paper, we first show a simpler method to add an arbitrary number of functionality constraints and cardinality constraints to sentences from the two-variable fragment while still guaranteeing polynomial-time inference. We then use this result to prove that WFOMC is domain-liftable for sentences from the two-variable fragment of first-order logic with counting quantifiers. The rest of the paper is structured as follows. Section 2 contains the background material needed for our technical results. In Sections 3-6, we work towards the proof of our main result which we give in Section 7. |
| Researcher Affiliation | Academia | Ondřej Kuželka EMAIL Faculty of Electrical Engineering Czech Technical University in Prague Prague, Czech Republic |
| Pseudocode | No | The paper describes methods and proofs using mathematical notation and textual descriptions, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper focuses on theoretical model counting and uses abstract logical frameworks and counting problems (e.g., counting k-regular graphs, anti-involutive functions on a domain of size 'n'). These are not traditional experimental datasets with concrete access information like links or citations to public repositories. |
| Dataset Splits | No | The paper is theoretical and does not use or analyze any datasets in a way that would require training/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or versions used for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical, presenting proofs and mathematical frameworks. It does not describe any experimental setups with hyperparameters, training configurations, or system-level settings. |