Collective hyper-heuristics for self-assembling robot behaviours

Shuang Yu, Andy Song, Aldeida Aleti

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Swarm robots are highly desirable in dealing with complex tasks. However, manual coding of individual robot behaviours and robot collaboration is not trivial especially under unknown and dynamic environments. This study introduced a hyper-heuristic methodology for this challenge, so robots can learn suitable behaviours during the process. The hyper-heuristic method creates actions based on a set of low-level heuristics and improves these actions through autonomous heuristic adjustment. A collective negotiation and updating mechanism is proposed so the robot swarm performance can be improved. We evaluate this method on the problem of building surface cleaning. Experiments show the effectiveness of the hyper-heuristic method and the collective learning mechanism.

Original languageEnglish
Title of host publicationPRICAI 2018: Trends in Artificial Intelligence
Subtitle of host publication15th Pacific Rim International Conference on Artificial Intelligence Nanjing, China, August 28–31, 2018 Proceedings, Part II
EditorsXin Geng, Byeong-Ho Kang
Place of PublicationCham Switzerland
Number of pages9
ISBN (Electronic)9783319973104
ISBN (Print)9783319973098
Publication statusPublished - 2018
EventPacific Rim International Conference on Artificial Intelligence 2018 - Nanjing, China
Duration: 28 Aug 201831 Aug 2018
Conference number: 15th

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific Rim International Conference on Artificial Intelligence 2018
Abbreviated titlePRICAI 2018
Internet address


  • Collective behaviours
  • Hyper heuristics
  • Swarm robots

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