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Regulation Function for Agent Adaptation Issues in Ambient Environment

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Published:16 January 2019Publication History

ABSTRACT

In this work we deal with action selection issues for software agent operating in ambient environment which is highly dynamic. Unpredictable events may occur and inappropriate actions may damage the software and its environment. Adaptation ability is a key requirement for such issues. We use multi-agent paradigm to address them. We propose a regulation function within agent architecture. It is a filter which acts on the stream of behaviour before it becomes or not action. The aim is to cope with environmental changes without the need to predict them precisely at design-time. To this end, we introduce the Influence-Reaction Model into the agent behaviour management. To facilitate its application, we implement the resulting architecture as a Java library called MECA. We experiment it with an agricultural robot moving through a field.

References

  1. C. Armbrust, L. Kiekbusch, and K. Berns. Using behaviour activity sequences for motion generation and situation recognition. In ICINCO 2011-Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics, Volume 2, Noordwijkerhout, The Netherlands, 28-31 July, 2011, pages 120--127, 2011.Google ScholarGoogle Scholar
  2. A. Brunete, M. Hernando, E. Gambao, and J. E. Torres. A behaviour-based control architecture for heterogeneous modular, multi-con gurable, chained micro-robots. Robotics and Autonomous Systems, 60(12):1607--1624, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Ferber and J.-P. Muller. Influences and reaction: a model of situated multiagent systems. In Proceedings of Second International Conference on Multi-Agent Systems (ICMAS-96), pages 72--79, 1996.Google ScholarGoogle Scholar
  4. D. Hanon, E. Grislin-Le Strugeon, and R. Mandiau. A behaviour based decisional model using vote. In 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), International Conference on Intelligent Agents, Web Technologies and Internet Commerce (IAWTIC 2005), 28--30 November 2005, Vienna, Austria, pages 39--44, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. N. Kalde, O. Simonin, and F. Charpillet. Comparison of classical and interactive multi-robot exploration strategies in populated environments. Acta Polytechnica, 55(3):154--161, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  6. F. Michel, A. Gouaich, and J. Ferber. Weak interaction and strong interaction in agent based simulations. In Multi-Agent-Based Simulation III, 4th International Workshop, pages 43--56. Springer, 2003Google ScholarGoogle Scholar
  7. Obdržálek. Mobile agents in multi-agent uav/ugv system. In Military Technologies (ICMT), 2017 International Conference on, pages 753--759. IEEE, 2017Google ScholarGoogle Scholar
  8. P. Pirjanian. Behavior coordination mechanisms-state-of-the-art. Technical report, Institute for Robotics and Intelligent Systems, University of Southern California, 1999Google ScholarGoogle Scholar
  9. L. S. Rocha and R. M. C. Andrade. Towards a formal model to reason about context-aware exception handling. In Proceedings of the 5th International Workshop on Exception Handling, WEH 2012, Zurich, Switzerland, June 9, 2012, pages 27--33. IEEE, 2012 Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Other conferences
      ICCMS '19: Proceedings of the 11th International Conference on Computer Modeling and Simulation
      January 2019
      253 pages
      ISBN:9781450366199
      DOI:10.1145/3307363

      Copyright © 2019 ACM

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      Publication History

      • Published: 16 January 2019

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