Synthesizing Resilient Strategies for Infinite-Horizon Objectives in Multi-Agent Systems
Authors: David Klaška, Antonín Kučera, Martin Kurečka, Vít Musil, Petr Novotný, Vojtěch Řehák
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimented with several benchmarks. The first is the open perimeter benchmark with 5 nodes discussed in Section 1, denoted by P5. We also consider generalizations to Pk (open perimeters of length k) for increasing odd values of k to address question (B) above. The next benchmark we consider is a 4 × 4 grid in which several edges were removed in a way preserving connectedness. This creates an irregular topology through which we address question (D). We consider several graphs of this form. Finally, we consider the triangle benchmark. |
| Researcher Affiliation | Academia | Masaryk University, Brno, Czech Republic |
| Pseudocode | Yes | Algorithm 1 Solution synthesis |
| Open Source Code | No | The paper does not provide a specific link or explicit statement for the open-source release of their code. |
| Open Datasets | No | The paper describes benchmarks like 'open perimeter', '4x4 grid', and 'triangle benchmark' but does not provide specific access information (links, DOIs, formal citations) for these as publicly available datasets. It mentions 'The exact topologies of these graphs are given in [Klaˇska et al., 2023, Appendix A].' which points to an extended version of the paper, but not directly to public dataset availability. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits, percentages, or counts for the data used in experiments. |
| Hardware Specification | Yes | The system setup was as follows: CPU: AMD Ryzen 9 3900X (12 cores); RAM: 32GB; Ubuntu 20.04. |
| Software Dependencies | No | Our implementation uses PYTORCH framework [Paszke et al., 2019] and its automatic differentiation with ADAM optimizer [Kingma and Ba, 2015]). While software is mentioned, specific version numbers for PyTorch or ADAM are not provided. |
| Experiment Setup | Yes | Each experiment is parameterized by the underlying graph G, the number of agents n, the number m of memory states per agent, the variance-punishing weight κ, and the α parameter weighting the value in case of agent failure. With an exception described later, the objective is to minimize the maximum over all vertices of the graph. ... All benchmarks were run with 600 steps. |