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..
Combining Opinion Pooling and Evidential Updating for Multi-Agent Consensus
Authors: Chanelle Lee, Jonathan Lawry, Alan Winfield
IJCAI 2018 | Venue PDF | 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 EMAIL, EMAIL |
| 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... |