Involving the Human User in the Control Architecture of an Autonomous Agent

  • Roberto Micalizio
  • Giancarlo Nuzzolo
  • Enrico Scala
  • Pietro Torasso
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 331)


The paper presents an architecture for an autonomous robotic agent, which carries on a plan in a partially observable environment. A Supervisor module is in charge of assuring the correct execution of the plan, possibly by inferring alternative recovery plans when unexpected contingencies occur. In the present paper we describe a control strategy where a human user is directly involved in the control loop, and plays the role of advisor by helping the robotic agent both for reducing ambiguity in the robot’s observations, and for selecting the preferred recovery plan.


Autonomous Agent Situation Awareness Plan Execution Replanning 


  1. 1.
    Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F.: An architecture for autonomy. International Journal of Robotics Research 17(4), 315–337 (1998)CrossRefGoogle Scholar
  2. 2.
    Galindo, C., Fernndez-Madrigal, J., Gonzlez, J., Saffiotti, A.: Robot task planning using semantic maps. Robotics and Autonomous Systems 56, 955–966 (2008)CrossRefGoogle Scholar
  3. 3.
    de Jonge, F., Roos, N., Witteveen, C.: Primary and secondary diagnosis of multi-agent plan execution. Journal of Autonomous Agent and MAS 18, 267–294 (2009)CrossRefGoogle Scholar
  4. 4.
    Micalizio, R., Torasso, P.: Monitoring the execution of a multi-agent plan: Dealing with partial observability. In: Proc. of ECAI 2008, pp. 408–412 (2008)Google Scholar
  5. 5.
    Sellner, B., Simmons, R., Singh, S.: User modeling for principled sliding autonomy in human-robot teams. Multi-Robot Systems. From Swarms to Intelligent Automata 3, 197–208 (2005)CrossRefGoogle Scholar
  6. 6.
    Fong, T., Thorpe, C., Baur, C.: Collaboration, dialogue, and human-robot interaction. In: Proc. 10th International Symposium on Robotics Research (2002)Google Scholar
  7. 7.
    Endsley, M.: Towards a theory of situation awareness in dynamic systems. Human Factors 37(1), 32–64Google Scholar
  8. 8.
    Bresina, J.L., Morris, P.H.: Mixed-initiative planning in space mission operations. AI Magazine 28(2), 75–88 (2007)Google Scholar
  9. 9.
    Micalizio, R., Torasso, P.: Recovery from plan failures in partially observable environments. In: Research and Development in Intelligent Systems XXVII, pp. 321–334 (2007)Google Scholar
  10. 10.
    Jensen, R.M., Veloso, M.M.: Obdd-based universal planning for synchronized agents in non-deterministic domains. Journal of Artificial Intelligence Research 13, 189–226 (2000)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Verma, V., Estlin, T., Jonsson, A., Pasareanu, C., Simmons, R., Tso, K.: Plan execution interchange language (plexil) for executable plan and command sequence. In: i-SAIRAS 2005 (2005)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Roberto Micalizio
    • 1
  • Giancarlo Nuzzolo
    • 1
  • Enrico Scala
    • 1
  • Pietro Torasso
    • 1
  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

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