HPLAN: Facilitating the Implementation of Joint Human-Agent Activities

  • Sebastian Ahrndt
  • Philipp Ebert
  • Johannes Fähndrich
  • Sahin Albayrak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8473)

Abstract

When it comes to planning for joint human-agent activities, one has to consider not only flexible plan execution and social constraints but also the dynamic nature of humans. This can be achieved by providing additional information about the characteristics of a human. As an example one need to take the physical and psychological condition of the elderly into consideration when developing collaborative applications like socially assistive robots. This work outlines Hplan, an extension to the agent-framework JIAC V, that takes this requirement into account. Hplan is strongly related to the conceptual model of dynamic planning components and integrates humans as avatars into a life cycle of planning, execution and learning.

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References

  1. 1.
    Ahrndt, S.: Improving human-aware planning. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) MATES 2013. LNCS, vol. 8076, pp. 400–403. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R.: Task planning for human-robot interaction. In: Bailly, G., Crowley, J.L., Privat, G. (eds.) Proc. of the sOc-EUSAI 2005, pp. 81–85. ACM Press (2005)Google Scholar
  3. 3.
    Alili, S., Warnier, M., Ali, M., Alami, R.: Planning and plan-execution for human-robot cooperative task achievement. In: Proc. of the 19th ICAPS, pp. 1–6 (2009)Google Scholar
  4. 4.
    Cirillo, M., Karlsson, L., Saffiotti, A.: Human-aware task planning: An application to mobile robots. ACM Trans. Intell. Syst. Technol. 1(2), 1–26 (2010)CrossRefGoogle Scholar
  5. 5.
    Clark, H.H.: Using Language. Cambridge Univ. Press (1996)Google Scholar
  6. 6.
    Claus, C., Boutilier, C.: Thy dynamics of reinforcement learning in cooperative multiagent systems. In: Proc. of the 15th AAAI, pp. 746–752 (1998)Google Scholar
  7. 7.
    Ebert, P.: Improving Human-Aware Planning through Reinforcement Learning – A Multi-Agent Based Approach. Master’s thesis, TU Berlin (2013)Google Scholar
  8. 8.
    Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. Artifical Intelligence Research 20, 61–124 (2003)MATHGoogle Scholar
  9. 9.
    Ghallab, M., Howe, A., Knoblock, C., et al.: PDDL – The Planning Domain Definition Language. Yale Center for Computational Vision and Control (1998)Google Scholar
  10. 10.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory & Practice. Morgan Kaufmann (2004)Google Scholar
  11. 11.
    Gupta, N., Nau, D.S.: On the complexity of blocks-world planning. Artifical Intelligence 56(2-3), 223–254 (1992)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  13. 13.
    Hirsch, B., Konnerth, T., Heßler, A.: Merging agents and services – the JIAC agent platform. In: Bordini, R.H., Dastani, M., Dix, J., Amal, E.F.S. (eds.) Multi-Agent Programming: Languages, Tools and Applications, pp. 159–185. Springer (2009)Google Scholar
  14. 14.
    Kirsch, A., Kruse, T., Mösenlechner, L.: An integrated planning and learning framework for human-robot interaction. In: Proc. of the 19th ICAPS, pp. 1–6 (2009)Google Scholar
  15. 15.
    Kirsch, A., Kruse, T., Sisbot, E.A., et al.: Plan-based control of joint human-robot activities. KI – Künstliche Intelligenz 24(3), 223–231 (2010)CrossRefGoogle Scholar
  16. 16.
    Klein, G., Woods, D.D., Bradshaw, J.M., Hoffmann, R.R., Feltovich, P.J.: Ten challenges for making automation a ‘team player’ in joint human-agent activity. Human-Centered Computing 19(6), 91–95 (2004)Google Scholar
  17. 17.
    Lützenberger, M., Küster, T., Konnerth, T., et al.: JIAC V –A MAS framework for industrial applications (extended abstract). In: Ito, T., Jonker, C., Gini, M., Shehory, O. (eds.) Proc. of the 12th AAMAS, pp. 1189–1190 (2013)Google Scholar
  18. 18.
    McCrea, R.R., John, O.P.: An introduction to the five-factor model and its applications. Personality 60(2), 175–215 (1992)CrossRefGoogle Scholar
  19. 19.
    Montreuil, V., Clodic, A., Alami, R.: Planning human centered robot activities. In: IEEE SMC, pp. 2618–2623 (2007)Google Scholar
  20. 20.
    Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T.: A human aware mobile robot motion planner. IEEE Transactions on Robotics 23(5), 874–883 (2007)CrossRefGoogle Scholar
  21. 21.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. MIT Press (May 1998)Google Scholar
  22. 22.
    Tapus, A., Matarić, M.J., Scassellati, B.: The grand challenges in socially assistive robotics. IEEE Robotics and Automation Magazin 14(1), 35–42 (2007)CrossRefGoogle Scholar
  23. 23.
    Wiener, J.M., Hanley, R.J., Clark, R., Nostrand, J.F.V.: Measuring the activities of daily living: Comparison across national surveys. Tech. rep., U.S. Department of Health and Human Services (1990), http://aspe.hhs.gov/daltcp/reports/meacmpes.pdf (last access: February 25, 2014)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sebastian Ahrndt
    • 1
  • Philipp Ebert
    • 1
  • Johannes Fähndrich
    • 1
  • Sahin Albayrak
    • 1
  1. 1.DAI-Laboratory, Faculty of Electrical Engineering and Computer ScienceTechnische Universität BerlinBerlinGermany

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