Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact - PEVA

SummaryThis artifact accompanies the PEVA submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes". It contains the implementation (switss-multi) of the presented techniques, that is, the computation of certificates, witnessing subsystems and schedulers for multi-objective queries in MDPs. Further, the artifact contains the PRISM models, PRISM properties and scripts bundled in a Docker image for completely reproducing the experimental results presented in Section 6. Additionally, it also contains the original raw experimental data presented in Section 6 and the corresponding analysis scripts. Lastly, we provide a documentation of our implementation switss-multi and describe how to use our tool via its command-line and programmatically via its Python interface.

Relation to paperThis artifact can be used to reproduce all the experimental results (including examples) presented in the paper, that is:- The toy examples presented in Example 12, Example 14, Example 22 and Example 34- Table 3 in Section 6- Table 4 in Section 6- Table 5 in Section 6- Table 8 in Section 6- Figure 9 in Section 6- Figure 10 in Section 6- Figure 11 in Section 6

StructureThis artifact consists of the following files and folders:- data: Contains original raw experimental data presented in Section 6. Additionally, the log files and scripts for summarizing the raw experimental data are provided.- switss-multi/experiments: Contains the PRISM models, PRISM properties (queries) and scripts for running the experiments.- switss-multi: The source code of the implementation of our presented techniques.- switss-multi-docs: A documentation of the Python API of switss-multi.- peva-docker-image.tar.gz: The compressed Docker image, with the installed implementation (switss-multi), PRISM models, PRISM properties and the scripts for running the experiments and analysing the raw experimental data. Moreover, it contains a copy of the data folder, in case you want to run the analysis scripts on the original data.- docker-results: An empty folder that will be populated with results when running the experiments and analysis with the provided Docker image.- LICENSE: The license of this artifact (MIT license).- GUROBI-EULA: The end-user license agreement of Gurobi (also see https://pypi.org/project/gurobipy/).- GPL-3.0: The GPL 3.0 license. It is included because our dependency Storm (https://www.stormchecker.org) is licensed under it.