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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Clark, H.H.: Using Language. Cambridge Univ. Press (1996)
Claus, C., Boutilier, C.: Thy dynamics of reinforcement learning in cooperative multiagent systems. In: Proc. of the 15th AAAI, pp. 746–752 (1998)
Ebert, P.: Improving Human-Aware Planning through Reinforcement Learning – A Multi-Agent Based Approach. Master’s thesis, TU Berlin (2013)
Fox, M., Long, D.: PDDL2.1: An extension to PDDL for expressing temporal planning domains. Artifical Intelligence Research 20, 61–124 (2003)
Ghallab, M., Howe, A., Knoblock, C., et al.: PDDL – The Planning Domain Definition Language. Yale Center for Computational Vision and Control (1998)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory & Practice. Morgan Kaufmann (2004)
Gupta, N., Nau, D.S.: On the complexity of blocks-world planning. Artifical Intelligence 56(2-3), 223–254 (1992)
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)
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)
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)
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)
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)
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)
McCrea, R.R., John, O.P.: An introduction to the five-factor model and its applications. Personality 60(2), 175–215 (1992)
Montreuil, V., Clodic, A., Alami, R.: Planning human centered robot activities. In: IEEE SMC, pp. 2618–2623 (2007)
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)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. MIT Press (May 1998)
Tapus, A., Matarić, M.J., Scassellati, B.: The grand challenges in socially assistive robotics. IEEE Robotics and Automation Magazin 14(1), 35–42 (2007)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahrndt, S., Ebert, P., Fähndrich, J., Albayrak, S. (2014). HPLAN: Facilitating the Implementation of Joint Human-Agent Activities. In: Demazeau, Y., Zambonelli, F., Corchado, J.M., Bajo, J. (eds) Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. PAAMS 2014. Lecture Notes in Computer Science(), vol 8473. Springer, Cham. https://doi.org/10.1007/978-3-319-07551-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-07551-8_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07550-1
Online ISBN: 978-3-319-07551-8
eBook Packages: Computer ScienceComputer Science (R0)