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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Verifying Fault-tolerance in Parameterised Multi-Agent Systems
Authors: Panagiotis Kouvaros, Alessio Lomuscio
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present an implementation and a case study identifying the threshold under which the alpha swarm aggregation algorithm is robust to faults against its temporal-epistemic specifications. |
| Researcher Affiliation | Academia | Panagiotis Kouvaros Department of Computing Imperial College London, UK University of Naples Federico II , Italy EMAIL Alessio Lomuscio Department of Computing Imperial College London, UK EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | We developed MCMAS-PFI, a toolkit realising the fault injection method described earlier, on top of MCMAS-P, an opensource model checker for the verification of PIISs [Kouvaros and Lomuscio, 2013]. The paper states MCMAS-P is open-source, but does not provide a direct link or explicit statement that MCMAS-PFI or the specific code for this paper's methodology is open-source or available. |
| Open Datasets | No | We adopt the typical setting employed to analyse the algorithm [Dixon et al., 2012]. In particular we assume that each robot moves on a two-dimensional arena and communicates with its peers via a wireless sensor of limited range. The arena is assumed to be finite and allowed to wrap around, i.e., for an α × α arena, the cell (1, 1) is to the right of the cell (1, α). [...] In the following we fix a 5 × 5 arena, assume a communication range of 1, and let α = 2. Initially the robots are connected, in forward mode, and collectively have every possible direction of movement. We refer to [Kouvaros and Lomuscio, 2015b] for the formal account of the PIISs modelling this instantiation of the algorithm. This describes the simulation setup, not a public dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | We developed MCMAS-PFI, a toolkit realising the fault injection method described earlier, on top of MCMAS-P, an opensource model checker for the verification of PIISs [Kouvaros and Lomuscio, 2013]. Specific version numbers for MCMAS-P or other software are not provided. |
| Experiment Setup | Yes | In the following we fix a 5 × 5 arena, assume a communication range of 1, and let α = 2. Initially the robots are connected, in forward mode, and collectively have every possible direction of movement. |