On Exploiting Hitting Sets for Model Reconciliation
Authors: Stylianos Loukas Vasileiou, Alessandro Previti, William Yeoh6514-6521
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude our paper with an empirical evaluation of the newly introduced approach on planning instances, where we show how it outperforms an existing stateof-the-art solver, and generic non-planning instances from recent SAT competitions, for which no other solver exists. |
| Researcher Affiliation | Collaboration | Stylianos Loukas Vasileiou,1 Alessandro Previti, 2 William Yeoh 1 1 Washington University in St. Louis 2 Ericsson Research {v.stylianos, wyeoh}@wustl.edu, alessandro.previti@ericsson.com |
| Pseudocode | Yes | Algorithm 1: Basic algorithm for computing the smallest support (one KB) |
| Open Source Code | Yes | The code repository is: https://github.com/vstylianos/aaai21. |
| Open Datasets | Yes | We used the actual IPC instances as the model of the agent (i.e., KBa)... |
| Dataset Splits | No | The paper mentions using 'IPC instances' and 'SAT competition instances' but does not specify any training, validation, or test dataset splits, percentages, or methodology for data partitioning. |
| Hardware Specification | Yes | We ran our experiments on a Mac Book Pro machine comprising of an Intel Core i7 2.6GHz processor with 16GB of memory. |
| Software Dependencies | No | Our implementation of Algorithm 2 is written in Python and integrates calls to SAT, MCS/MUS, and minimal hitting set oracles through the Py SAT toolkit (Ignatiev, Morgado, and Marques-Silva 2018). |
| Experiment Setup | Yes | The time limit was set to 1500s. |