Combining Opinion Pooling and Evidential Updating for Multi-Agent Consensus
Authors: Chanelle Lee, Jonathan Lawry, Alan Winfield
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then use simulation experiments to show that pooling operators can provide a mechanism by which a limited amount of direct evidence can be efficiently propagated through a population of agents so that an appropriate consensus is reached. In particular, we explore the convergence properties of a parameterised family of operators with a range of evidence propagation strengths. |
| Researcher Affiliation | Academia | 1 Bristol Robotics Laboratory 2 University of Bristol 3 The University of the West of England, Bristol {c.l.lee, Alan.Winfield}@brl.ac.uk, J.Lawry@bristol.ac.uk |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not state that it provides open-source code for the described methodology. The acknowledgements mention 'All underlying data is included in full within the paper', implying results are in the text, not external code. |
| Open Datasets | No | A population of 100 agents is initialised with probabilities of H1 randomly chosen from [0, 1]. This indicates a synthetic, self-generated dataset, not a publicly available one with concrete access information. |
| Dataset Splits | No | The paper describes a simulation setup for agent behavior and a consensus criterion, but it does not specify training, validation, or test dataset splits in the context of machine learning model evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software components with version numbers. |
| Experiment Setup | Yes | A population of 100 agents is initialised with probabilities of H1 randomly chosen from [0, 1]. At each iteration, the population is permuted to emulate movement... A pool of k agents are then chosen at random from the population... every agent also has a probability ϵ of directly receiving the evidence E = H1... We take α = 0.1... |