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..
Epistemic Quantified Boolean Logic: Expressiveness and Completeness Results
Authors: Francesco Belardinelli, Wiebe van der Hoek
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce epistemic quantified boolean logic (EQBL), an extension of propositional epistemic logic with quantification over propositions. We show that EQBL can express relevant properties about agents knowledge in multi-agent contexts, such as agent a knows as much as agent b . We analyse the expressiveness of EQBL through a translation into monadic second-order logic, and provide completeness results w.r.t. various classes of Kripke frames. Finally, we prove that model checking EQBL is PSPACE-complete. |
| Researcher Affiliation | Academia | Francesco Belardinelli Laboratoire IBISC, Universit e d Evry, France EMAIL Wiebe van der Hoek Department of Computer Science University of Liverpool, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 Computation of the satisfaction set [[φ]]M switch (φ): case : return ; case p: return V (p); case ψ: return W \ [[ψ]]M; case ψ ψ : return [[ψ]]M [[ψ ]]M; case Kaψ: return {w W | Ra(w) [[ψ]]M}; case Cψ: return {w W | (S a Ag Ra) (w) [[ψ]]M}; case pψ: return T U D{[[ψ]]M ) | M = F, V p U }; |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. The card game example is illustrative, not a dataset used for training or evaluation. |
| Dataset Splits | No | The paper does not conduct empirical experiments, and therefore no training, validation, or test dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical, focusing on logic, expressiveness, completeness, and complexity analysis. It does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and describes a formal logic. There are no mentions of specific software libraries, frameworks, or tools with version numbers that would be required for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, thus no experimental setup details, hyperparameters, or training configurations are provided. |