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International Conference on Agents and Artificial Intelligence

ICAART 2014: Agents and Artificial Intelligence pp 262–279Cite as

ReCon: An Online Task ReConfiguration Approach for Robust Plan Execution

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8946))

Abstract

The paper presents an approach for the robust plan execution in presence of consumable and continuous resources. Plan execution is a critical activity since a number of unexpected situations could prevent the feasibility of tasks to be accomplished; however, many robotic scenarios (e.g. in space exploration) disallow robotic systems to perform significant deviations from the original plan formulation. In order to both (i) preserve the “stability” of the current plan and (ii) provide the system with a reasonable level of autonomy in handling unexpected situations, an innovative approach based on task reconfiguration is presented. Exploiting an enriched action formulation grounding on the notion of execution modalities, ReCon replaces the replanning mechanism with a novel reconfiguration mechanism, handled by means of a CSP solver. The paper studies the system for a typical planetary rover mission and provides a rich experimental analysis showing that, when the anomalies refer to unexpected resources consumption, the reconfiguration is not only more efficient but also more effective than a plan adaptation mechanism. The experiments are performed by evaluating the recovery performances depending on constraints on computational costs.

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Notes

  1. 1.

    The notion of alternative (sub)plans is also presented for (off-line) scheduling; for details see [1].

  2. 2.

    The software is at disposal at http://www.emn.fr/z-info/choco-solver/, while the work has been presented in [23].

  3. 3.

    To simplify the picture, we show in the rover’s status just a subset of the whole status variables.

  4. 4.

    The slowdown command of the rover may be the consequence of a reactive supervisor, which operates as a continuous controller as shown in [21].

  5. 5.

    The plan can be also generated automatically by exploiting a numeric planner system, properly modified to handle actions with modalities (e.g., the Metric-FF planning system [17] or LPG [16]).

  6. 6.

    Alternative CSP conversions are possible; for instance see [2].

  7. 7.

    LPG-ADAPT, [15], is the plan adaptation extension of LPG, [16], one of the more awarded systems throughout the planning competitions of the last decade. LPG-ADAPT can be considered the state of the art in the context of plan adaptation.

  8. 8.

    http://www.di.unito.ittextasciitildescala.

  9. 9.

    The Choco Solver implements the state of the art algorithms for constraint programming and has already been used in space applications, see [6]. Choco can be downloaded at http://www.emn.fr/z-info/choco-solver/.

  10. 10.

    Experiments have run on a 2.53 GHz Intel(R) Core(TM)2 Duo processor with 4 GB.

References

  1. Barták, R., Čepek, O., Hejna, M.: Temporal reasoning in nested temporal networks with alternatives. In: Fages, F., Rossi, F., Soliman, S. (eds.) CSCLP 2007. LNCS (LNAI), vol. 5129, pp. 17–31. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Barták, R., Toropila, D.: Solving sequential planning problems via constraint satisfaction. Fundamamenta Informaticae 99(2), 125–145 (2010)

    MathSciNet  MATH  Google Scholar 

  3. Block, S.A., Wehowsky, A.F., Williams, B.C.: Robust execution of contingent, temporally flexible plans. In: Proceedings of National Conference on Artificial Intelligence (AAAI 2006), pp. 802–808 (2006)

    Google Scholar 

  4. Brenner, M., Nebel, B.: Continual planning and acting in dynamic multiagent environments. J. Auton. Agent. Multiagent Syst. 19(3), 297–331 (2009)

    Article  Google Scholar 

  5. Calisi, D., Iocchi, L., Nardi, D., Scalzo, C., Ziparo, V.A.: Context-based design of robotic systems. Robot. Auton. Syst. (RAS) 56(11), 992–1003 (2008)

    Article  Google Scholar 

  6. Cesta, A., Fratini, S.: The timeline representation framework as a planning and scheduling software development environment. In: Proceedings of P&S Special Interest Group Workshop (PLANSIG-10) (2009)

    Google Scholar 

  7. Chien, S., Johnston, M., Frank, J., Giuliano, M., Kavelaars, A., Lenzen, C., Policella, N.: A generalized timeline representation, services, and interface for automating space mission operations. Technical Report JPL TRS 1992+, Ames Research Center; Jet Propulsion Laboratory, June 2012

    Google Scholar 

  8. Conrad, P.R., Williams, B.C.: Drake: an efficient executive for temporal plans with choice. J. Artif. Intell. Res. 42, 607–659 (2011)

    MathSciNet  MATH  Google Scholar 

  9. desJardins, M., Durfee, E.H., Ortiz Jr., C.L., Wolverton, M.: A survey of research in distributed, continual planning. AI Mag. 20(4), 13–22 (1999)

    Google Scholar 

  10. Fox, M., Gerevini, A., Long, D., Serina, I.: Plan stability: replanning versus plan repair. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2006), pp. 212–221 (2006)

    Google Scholar 

  11. Fox, M., Long, D.: Pddl2.1: an extension to PDDL for expressing temporal planning domains. J. Artif. Intell. Res. 20, 61–124 (2003)

    MATH  Google Scholar 

  12. Fratini, S., Pecora, F., Cesta, A.: Unifying planning and scheduling as timelines in a component-based perspective. Arch. Control Sci. 18(2), 231–271 (2008)

    MathSciNet  MATH  Google Scholar 

  13. Garrido, A., Guzman, C., Onaindia, E.: Anytime plan-adaptation for continuous planning. In: Proceedings of P&S Special Interest Group Workshop (PLANSIG 2010) (2010)

    Google Scholar 

  14. Gerevini, A., Serina, I.: Efficient plan adaptation through replanning windows and heuristic goals. Fundamenta Informaticae 102(3–4), 287–323 (2010)

    MATH  Google Scholar 

  15. Gerevini, A., Saetti, A., Serina, I.: Case-based planning for problems with real-valued fluents: kernel functions for effective plan retrieval. In: Proceedings of European Conference on AI (ECAI 2012), pp. 348–353 (2012)

    Google Scholar 

  16. Gerevini, A., Saetti, I., Serina, A.: An approach to efficient planning with numerical fluents and multi-criteria plan quality. Artif. Intell. 172(8–9), 899–944 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  17. Hoffmann, J.: The metric-FF planning system: translating “ignoring delete lists” to numeric state variables. J. Artif. Intell. Res. 20, 291–341 (2003)

    MATH  Google Scholar 

  18. van der Krogt, R., de Weerdt, M.: Plan repair as an extension of planning. In: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2005), pp. 161–170 (2005)

    Google Scholar 

  19. Lopez, A., Bacchus, F.: Generalizing graphplan by formulating planning as a CSP. In: Proceedings of International Conference on Artificial Intelligence (IJCAI 2003), pp. 954–960 (2003)

    Google Scholar 

  20. Micalizio, R.: Action failure recovery via model-based diagnosis and conformant planning. Comput. Intell. 29(2), 233–280 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  21. Micalizio, R., Scala, E., Torasso, P.: Intelligent supervision for robust plan execution. In: Pirrone, R., Sorbello, F. (eds.) AI*IA 2011. LNCS, vol. 6934, pp. 151–163. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  22. Muscettola, N.: Hsts: integrating planning and scheduling. Technical Report CMU-RI-TR-93-05, Robotics Institute, Pittsburgh, PA, March 1993

    Google Scholar 

  23. Narendra, J., Rochart, G., Lorca, X.: Choco: an open source java constraint programming library. In: CPAIOR 2008 Workshop on Open-Source Software for Integer and Constraint Programming (OSSICP 2008), pp. 1–10 (2008)

    Google Scholar 

  24. Policella, N., Cesta, A., Oddi, A., Smith, S.: Solve-and-robustify. J. Sched. 12, 299–314 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  25. Scala, E., Micalizio, R., Torasso, P.: Robust plan execution via reconfiguration and replanning. AI Communications, to appear (2014)

    Google Scholar 

  26. Scala, E.: Numeric kernel for reasoning about plans involving numeric fluents. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 263–275. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  27. Scala, E.: Numerical kernels for monitoring and repairing plans involving continuous and consumable resources. In: Proceedings of International Conference on Agents and Artificial Intelligence (ICAART 2013), pp. 531–534 (2013)

    Google Scholar 

  28. Scala, E.: Reconfiguration and replanning for robust execution of plans involving continuous and consumable resources. Ph.D. thesis, Department of Computer Science - University of Turin (2013)

    Google Scholar 

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Acknowledgements

We would like to thank the Choco’s team for making freely available the CSP solver, Joerg Hoffman for the Metric-FF planning system as well as Alfonso Gerevini, Alessandro Saetti and Ivan Serina for the LPG-ADAPT system.

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Authors and Affiliations

  1. Dipartimento di Informatica, Universita’ di Torino, Turin, Italy

    Enrico Scala, Roberto Micalizio & Pietro Torasso

Authors
  1. Enrico Scala
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  2. Roberto Micalizio
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  3. Pietro Torasso
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Corresponding author

Correspondence to Enrico Scala .

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Editors and Affiliations

  1. LERIA - UFR Sciences, Angers, France

    Béatrice Duval

  2. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  3. LERIA - UFR Sciences, Angers, France

    Stephane Loiseau

  4. INSTICC, Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Scala, E., Micalizio, R., Torasso, P. (2015). ReCon: An Online Task ReConfiguration Approach for Robust Plan Execution. In: Duval, B., van den Herik, J., Loiseau, S., Filipe, J. (eds) Agents and Artificial Intelligence. ICAART 2014. Lecture Notes in Computer Science(), vol 8946. Springer, Cham. https://doi.org/10.1007/978-3-319-25210-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-25210-0_16

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