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].
On the Disruptive Effectiveness of Automated Planning for LTLf-Based Trace Alignment
Authors: Giuseppe De Giacomo, Fabrizio Maria Maggi, Andrea Marrella, Fabio Patrizi
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report an in-depth experimental study that supports this claim. |
| Researcher Affiliation | Academia | Giuseppe De Giacomo Sapienza Universit a di Roma, Italy EMAIL Fabrizio Maria Maggi University of Tartu, Estonia EMAIL Andrea Marrella Sapienza Universit a di Roma, Italy EMAIL Fabio Patrizi Sapienza Universit a di Roma, Italy EMAIL |
| Pseudocode | No | No explicit pseudocode or clearly labeled algorithm block was found. The paper includes PDDL examples, which are concrete syntax, not abstract pseudocode. |
| Open Source Code | No | The paper mentions developing a tool ('We have developed a planning-based alignment tool as a standard Java application that implements the approach discussed in Sections 4 and 5.') but does not provide any link or explicit statement about the public availability of its source code. |
| Open Datasets | No | The paper mentions a 'real-life log' from 'a Dutch ๏ฌnancial institute' and 'synthetic logs' generated using 'the log generator presented in (Di Ciccio et al. 2015)'. However, it does not provide concrete access information (link, DOI, repository, or explicit statement of public availability) for either the real-life or synthetic datasets used in the experiments. |
| Dataset Splits | No | The paper describes the datasets used (real-life and synthetic logs) and their characteristics, but does not specify any explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined split citations). |
| Hardware Specification | Yes | We performed our experiments with a machine consisting of an Intel Core i7-4770S CPU 3.10GHz Quad Core and 4GB RAM. |
| Software Dependencies | No | The paper mentions using 'FAST-DOWNWARD (Helmert 2006)' and 'SYMBA*2 (Torralba et al. 2014)' planning systems, and the implementation is a 'standard Java application'. However, specific version numbers for these software dependencies (e.g., for Fast-Downward, SYMBA*2, or Java) are not provided. |
| Experiment Setup | Yes | We used a standard cost function with unit costs for any alignment step that adds/removes activities in/from the input trace, and cost 0 for synchronous moves. |