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].
Dynamic logic of parallel propositional assignments and its applications to planning
Authors: Andreas Herzig, Frédéric Maris, Julien Vianey
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a dynamic logic with parallel composition and two kinds of nondeterministic composition, exclusive and inclusive. We show PSPACE completeness of both the model checking and the satisfiability problem and apply our logic to sequential and parallel classical planning where actions have conditional effects. |
| Researcher Affiliation | Academia | 1IRIT-CNRS 2IRIT-Univ. Toulouse EMAIL |
| Pseudocode | No | The paper provides formal definitions for the interpretation of DL-PPA programs in Figure 1 and translation rules in Figure 2, but these are semantic and translation rules, not pseudocode or algorithm blocks describing a computational procedure. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | This is a theoretical paper focusing on logic and complexity, not empirical experiments. No datasets are mentioned for training. |
| Dataset Splits | No | This is a theoretical paper focusing on logic and complexity, not empirical experiments. Therefore, no dataset splits for validation are mentioned. |
| Hardware Specification | No | This is a theoretical paper; therefore, there is no information provided regarding specific hardware used for any experiments. |
| Software Dependencies | No | This is a theoretical paper and does not describe any specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | This is a theoretical paper and does not describe an empirical experimental setup, hyperparameters, or training settings. |