Logics for Sizes with Union or Intersection

Authors: Caleb Kisby, Saul Blanco, Alex Kruckman, Lawrence Moss2870-2876

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This paper presents the most basic logics for reasoning about the sizes of sets that admit either the union of terms or the intersection of terms. That is, our logics handle assertions All x y and At Least x y, where x and y are built up from basic terms by either unions or intersections. We present a sound, complete, and polynomial-time decidable proof system for these logics.
Researcher Affiliation Academia Caleb Kisby,1 Sa ul A. Blanco,1 Alex Kruckman,2 Lawrence S. Moss3 1Department of Computer Science, Indiana University, Bloomington, IN 47408, USA {cckisby, sblancor}@indiana.edu 2Department of Mathematics and Computer Science, Wesleyan University, Middletown, CT 06459, USA akruckman@wesleyan.edu 3Department of Mathematics, Indiana University, Bloomington, IN 47405, USA lmoss@indiana.edu
Pseudocode No The paper provides figures (Figure 1 and Figure 2) that list formal rules for the logics, but these are not labeled 'Pseudocode' or 'Algorithm' blocks.
Open Source Code No The paper does not contain an unambiguous statement or link indicating the release of source code for the methodology described.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training; therefore, no information on public dataset availability is provided.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and focuses on logical systems and proofs, with no mention of specific hardware used for experiments.
Software Dependencies No The paper is theoretical and does not specify any software dependencies with version numbers needed to replicate the work.
Experiment Setup No The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations.