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].
Steady-State Strategy Synthesis for Swarms of Autonomous Agents
Authors: Martin Jonáš, Antonín Kučera, Vojtěch Kůr, Jan Mačák
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm experimentally on a series of randomly generated instances, and we show that it clearly outperforms a naive algorithm based on strategy sharing (see Section 5 for details). |
| Researcher Affiliation | Academia | Martin Jon aˇs , Anton ın Kuˇcera , Vojtˇech K ur and Jan Maˇc ak Faculty of Informatics, Masaryk University, Czechia EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Incremental Steady-State Synthesis Algorithm |
| Open Source Code | Yes | We implemented both algorithms in a simple open source Python tool that uses Gurobi [Gurobi Optimization, LLC, 2024] to solve the linear programming problems. The tool is available from Git Lab3. (Footnote 3: https://gitlab.fi.muni.cz/formela/multi-agent-steady-state-synthesis) |
| Open Datasets | No | The paper states: "For simplicity, we perform our experiments on graphs." and "To avoid any systematic bias, we randomly generated two families of strongly connected input graphs: aperiodic and periodic." This indicates that the datasets used were generated by the authors for the purpose of the experiments and not external publicly available datasets with concrete access information. |
| Dataset Splits | No | The paper describes generating graphs and objectives for benchmarks, but it does not specify any training/test/validation splits for these generated instances. The experiments directly evaluate the algorithms on these generated instances rather than using predefined splits. |
| Hardware Specification | Yes | We executed both algorithms on each benchmark with timeout 120 seconds of wall time on a Linux computer with AMD Ryzen 7 PRO 5750G CPU and 32 GB of RAM. |
| Software Dependencies | Yes | We implemented both algorithms in a simple open source Python tool that uses Gurobi [Gurobi Optimization, LLC, 2024] to solve the linear programming problems. |
| Experiment Setup | Yes | The algorithm examines all possible allocations for vℓ+1, i.e., all cyclic classes C in all Dq. For given C and Dq, the procedure STRATEGYOFLP constructs the linear program of Fig. 4 and returns the full MR strategy σ = (v0, κ), where v0 C and κ(u)(v) is the normalized value of xu,v attained by solving the program. We also measured the runtime for both algorithms. The measured wall times are summarized in Table 1. ... timeout 120 seconds of wall time. |