Hybrid-order Network Consensus for Distributed Multi-agent Systems
Authors: Guangqiang Xie, Junyu Chen, Yang Li
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we use ten topologies of different shapes, densities and ranges to comprehensively analyze the performance of our proposed algorithms. The simulation results show that the hybrid higher-order network can effectively enhance the consensus of the multi-agent system in different network topologies. |
| Researcher Affiliation | Academia | Guangqiang Xie EMAIL Junyu Chen EMAIL Yang Li EMAIL College of Computer Science, Guangdong University of Technology, Mail Stop: 510006, No.100 Waihuanxi Road, Guangzhou HEMC, Guangdong |
| Pseudocode | Yes | Algorithm 1 The update process of agent i at time t based on MWMS: MWMS-S. Input: Ni(t) Output: xi(t + 1) ... Algorithm 2 The iterative of agent i based on MWMS: MWMS-J. Input: Ni(t) Output: xi(t + 1) ... |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide links to a code repository. |
| Open Datasets | No | In our experiments, we use ten topologies of different shapes, densities and ranges to comprehensively analyze the performance of our proposed algorithms. The topologies are described in the paper (e.g., Top-1, Top-2, etc.), indicating they are generated or constructed for the experiments, not external publicly available datasets with access information. The paper states, 'the topologies in this subsection are described in (Vicsek, Czir ok, Ben-Jacob, Cohen, & Shochet, 1995) whose simulation are carried out in a square shaped of linear size L and with density ρ = n/L2.' This refers to a method of generating topologies, not a publicly accessible dataset itself. |
| Dataset Splits | No | The paper describes how various network topologies are generated for simulation (e.g., 'Random place n = ρ L2 agents in a circular area'), but it does not specify any training, validation, or test splits. The experiments analyze performance on these generated topologies as a whole, rather than using distinct data subsets for different stages. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers (e.g., programming languages, libraries, or frameworks with versions) used for implementing the methodology or running the experiments. |
| Experiment Setup | Yes | In our experiments, 11 different values of α are tested, that is α {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. In addition, rc = 1 and ε = 1/n. ... The topologies in the square range with L = 10rc are described as follows. ... Firstly, we place the agent in the square area of L = 10rc. In order to ensure the connectivity of network topology, we divided the square area into several 0.5rc 0.5rc small squares. The number of agents in each 0.5rc 0.5rc small square is 1, 2, 3, 4 and 5 respectively. In other words, the density is 4/r2 c, 8/r2 c, 12/r2 c, 16/r2 c, 20/r2 c, respectively. ... Secondly, agents are distributed in five different ranges of L = 5rc, 10rc, 15rc, 20rc, 25rc with the same ρ = 4/r2 c. |