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Real-time learning and planning in environments with swarms: a hierarchical and a parameter-based simulation approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Lukasz Pelcner
  • Dr Shaling Li
  • Matheus Aparecido do Carmo Alves
  • Leandro Marcolino
  • Alex Collins
Swarms can be applied in many relevant domains, such as patrolling or rescue. They usually follow simple local rules, leading to complex emergent behavior. Given their wide applicability, an agent may need to take decisions in an environment containing a swarm that is not under its control, and that may even be an antagonist. Predicting the behavior of each swarm member is a great challenge, and must be done under real time constraints, since they usually move constantly following quick reactive algorithms. We propose the first two solutions for this novel problem, showing integrated on-line learning and planning for decision-making with unknown swarms: (i) we learn an ellipse abstraction of the swarm based on statistical models, and predict its future parameters using time-series; (ii) we learn algorithm parameters followed by each swarm member, in order to directly simulate them. We find in our experiments that we are significantly faster to reach an objective than local repulsive forces, at the cost of success rate in some situations. Additionally, we show that this is a challenging problem for reinforcement learning.
Original languageEnglish
Title of host publicationAAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Pages1019-1027
Number of pages9
ISBN (Print)978-1-4503-7518-4
Publication statusPublished - 9 May 2020
EventInternational Conference on Autonomous Agents and Multiagent Systems 2020 - Auckland, New Zealand
Duration: 9 May 202013 May 2020
Conference number: 19th
https://aamas2020.conference.auckland.ac.nz/

Publication series

NameProceedings of AAMAS 2020
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
ISSN (Print)2523-5699

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems 2020
Abbreviated titleAAMAS 2020
CountryNew Zealand
CityAuckland
Period9/05/2013/05/20
Internet address

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ID: 20918633