Probabilistic Temporal Logic for Reasoning about Bounded Policies
Authors: Nima Motamed, Natasha Alechina, Mehdi Dastani, Dragan Doder, Brian Logan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 φ0 function PLBP-LABEL(M, φ0) ... Algorithm 2 Computing the measure of paths function MEASURE(q, Φ, n, PΦ) |
| 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. |