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..
Probabilistic Temporal Logic for Reasoning about Bounded Policies
Authors: Nima Motamed, Natasha Alechina, Mehdi Dastani, Dragan Doder, Brian Logan
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper introduces the Probabilistic Logic of Bounded Policies (PLBP), a novel probabilistic temporal logic, and focuses on proving its computational properties. It states: "We prove that the satisfiability problem for our logic is decidable, and that its model checking problem is PSPACE-complete." This indicates a theoretical contribution involving proofs and complexity analysis, rather than empirical studies or data analysis. |
| Researcher Affiliation | Academia | Nima Motamed1 , Natasha Alechina1 , Mehdi Dastani1 , Dragan Doder1 and Brian Logan1,2 1Utrecht University 2University of Aberdeen |
| Pseudocode | Yes | Algorithm 1 Labelling Ļ0 function PLBP-LABEL(M, Ļ0) ... Algorithm 2 Computing the measure of paths function MEASURE(q, Ī¦, n, PĪ¦) |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or links to a code repository for the described logic or its implementation. |
| Open Datasets | No | The paper presents theoretical work on a formal logic and does not involve training models on datasets. Therefore, no public dataset information or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. Therefore, no dataset split information for validation is provided. |
| Hardware Specification | No | The paper presents theoretical work and does not describe any computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper presents theoretical work on a formal logic and does not describe an implementation that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not detail any empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |