Stable Belief Estimation in Shepherd-Assisted Swarm Collective Decision Making

Aya Hussein, Hussein A. Abbass

Research output: A Conference proceeding or a Chapter in BookChapterpeer-review

2 Citations (Scopus)

Abstract

Swarm collective decision making refers to the case where a swarm needs to make a decision based on different pieces of evidence collected by its individuals. This problem has been investigated by several recent studies which proposed strategies to enable the swarm to perform fast and accurate collective decision making. However, the performance of these strategies (in terms of its accuracy, speed and level of consensus) suffers significantly in complex environments. The aim of our work is to propose a collective decision-making strategy that promises a consistent performance across different levels of scenario complexity and achieves superiority over the existing strategies in highly complex scenarios. To achieve this aim, our proposed algorithm employs a shepherding agent to boost the performance of the swarm. The swarm members are only responsible for sensing the state of a feature distributed in the environment. Only the shepherd needs to be able to process position and navigation abilities to collect the swarm. The algorithm consists of two phases: exploration and belief sharing. In the exploration phase, swarm members navigate through an environment and sense its features. Then, in the belief sharing phase, a shepherding agent collects the swarm members together so that they can share their estimates and calculate their decisions. The results demonstrate that the proposed shepherding algorithm succeeds across different levels of scenario complexity. Additionally, the approach achieves high levels of accuracy and consensus in complex non-homogeneous environments where the baseline state-of-the-art algorithm fails.

Original languageEnglish
Title of host publicationShepherding UxVs for Human-Swarm Teaming
Subtitle of host publicationAn Artificial Intelligence Approach to Unmanned X Vehicles
EditorsHussein A. Abbass, Robert A. Hunjet
PublisherSpringer - Adis
Chapter8
Pages165-185
Number of pages21
ISBN (Electronic)9783030608989
ISBN (Print)9783030608972
DOIs
Publication statusPublished - 2021
Externally publishedYes

Publication series

NameUnmanned System Technologies
ISSN (Print)2523-3734
ISSN (Electronic)2523-3742

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