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International Conference on Agents and Artificial Intelligence

ICAART 2020: Agents and Artificial Intelligence pp 254–279Cite as

Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12613))

Abstract

Automated planning deals with the problem of finding a sequence of actions leading from a given state to a desired state. The state-of-the-art automated planning techniques exploit informed forward search guided by a heuristic which is used to estimate a distance from a state to a goal state.

In this paper, we present a technique to automatically construct an efficient heuristic for a given domain. The proposed approach is based on training a deep neural network using a set of solved planning problems as training data. We use a novel way of extracting features for states developed specifically for planning applications. Our experiments show that the technique is competitive with state-of-the-art domain-independent heuristic. We also introduce a theoretical framework to formally analyze behaviour of learned heuristics. We state and prove several theorems that establish bounds on the worst-case performance of learned heuristics.

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Notes

  1. 1.

    api.planning.domains/json/classical/problems/17.

  2. 2.

    api.planning.domains/json/classical/problems/112.

References

  1. Arfaee, S.J., Zilles, S., Holte, R.C.: Bootstrap learning of heuristic functions. In: Felner, A., Sturtevant, N.R. (eds.) Proceedings of the Third Annual Symposium on Combinatorial Search, SOCS 2010. AAAI Press (2010)

    Google Scholar 

  2. Arfaee, S.J., Zilles, S., Holte, R.C.: Learning heuristic functions for largestate spaces. Artif. Intell. 175(16), 2075–2098 (2011)

    Article  Google Scholar 

  3. Bisson, F., Larochelle, H., Kabanza, F.: Using a recursive neural network to learn an agent’s decision model for plan recognition. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  4. Brunetto, R., Trunda, O.: Deep heuristic-learning in the Rubik’s cube domain: an experimental evaluation. In: Hlaváčová, J. (ed.) Proceedings of the 17th Conference ITAT 2017, pp. 57–64. CreateSpace Independent Publishing Platform (2017)

    Google Scholar 

  5. Cenamor, I., De La Rosa, T., Fernández, F.: Learning predictive models to configure planning portfolios. In: Proceedings of the 4th Workshop on Planning and Learning (ICAPS-PAL 2013) (2013)

    Google Scholar 

  6. Chen, H.C., Wei, J.D.: Using neural networks for evaluation in heuristic search algorithm. In: AAAI (2011)

    Google Scholar 

  7. Ferber, P., Helmert, M., Hoffmann, J.: Neural network heuristics for classical planning: a study of hyperparameter space. In: ECAI (2020)

    Google Scholar 

  8. Fink, M.: Online learning of search heuristics. In: Artificial Intelligence and Statistics, pp. 115–122 (2007)

    Google Scholar 

  9. Geissmann, C.: Learning heuristic functions in classical planning. Master’s thesis, University of Basel, Switzerland (2015)

    Google Scholar 

  10. Goldberg, Y.: Neural network methods for natural language processing. Synth. Lect. Hum. Lang. Technol. 10(1), 1–309 (2017)

    Article  Google Scholar 

  11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  12. Groshev, E., Goldstein, M., et al.: Learning generalized reactive policies using deep neural networks. In: Symposium on Integrating Representation, Reasoning, Learning, and Execution for Goal Directed Autonomy (2017)

    Google Scholar 

  13. Groshev, E., Tamar, A., Goldstein, M., Srivastava, S., Abbeel, P.: Learning generalized reactive policies using deep neural networks. In: 2018 AAAI Spring Symposium Series (2018)

    Google Scholar 

  14. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York (2001). https://doi.org/10.1007/978-0-387-21606-5

    Book  MATH  Google Scholar 

  15. Helmert, M.: Understanding Planning Tasks: Domain Complexity and Heuristic Decomposition. LNCS (LNAI), vol. 4929. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77723-6

    Book  MATH  Google Scholar 

  16. Helmert, M.: Concise finite-domain representations for PDDL planning tasks. Artif. Intell. 173(5), 503–535 (2009). https://doi.org/10.1016/j.artint.2008.10.013. http://www.sciencedirect.com/science/article/pii/S0004370208001926. Advances in Automated Plan Generation

    Article  MathSciNet  MATH  Google Scholar 

  17. Hoffmann, J., Nebel, B.: The FF planning system: fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 253–302 (2001)

    Article  Google Scholar 

  18. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991). https://doi.org/10.1016/0893-6080(91)90009-T. http://www.sciencedirect.com/science/article/pii/089360809190009T

    Article  MathSciNet  Google Scholar 

  19. Jiménez, S., De la Rosa, T., Fernández, S., Fernández, F., Borrajo, D.: A review of machine learning for automated planning. Knowl. Eng. Rev. 27(4), 433–467 (2012)

    Article  Google Scholar 

  20. Konidaris, G., Kaelbling, L.P., Lozano-Perez, T.: From skills to symbols: learning symbolic representations for abstract high-level planning. J. Artif. Intell. Res. 61, 215–289 (2018)

    Article  MathSciNet  Google Scholar 

  21. Martín, M., Geffner, H.: Learning generalized policies from planning examples using concept languages. Appl. Intell. 20(1), 9–19 (2004)

    Article  Google Scholar 

  22. Nau, D., Ghallab, M., Traverso, P.: Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco (2004)

    MATH  Google Scholar 

  23. Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. The Addison-Wesley Series in Artificial Intelligence. Addison-Wesley (1984). https://books.google.cz/books?id=0XtQAAAAMAAJ

  24. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2010)

    MATH  Google Scholar 

  25. Samadi, M., Felner, A., Schaeffer, J.: Learning from multiple heuristics. In: Fox, D., Gomes, C.P. (eds.) AAAI, pp. 357–362. AAAI Press (2008)

    Google Scholar 

  26. Takahashi, T., Sun, H., Tian, D., Wang, Y.: Learning heuristic functions for mobile robot path planning using deep neural networks. In: Proceedings of the International Conference on Automated Planning and Scheduling. vol. 29, pp. 764–772 (2019)

    Google Scholar 

  27. Thayer, J., Dionne, A., Ruml, W.: Learning inadmissible heuristics during search. In: Proceedings of International Conference on Automated Planning and Scheduling (2011)

    Google Scholar 

  28. Toyer, S., Trevizan, F., Thiébaux, S., Xie, L.: Action schema networks: generalised policies with deep learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  29. Trunda, O., Barták, R.: Deep learning of heuristics for domain-independent planning. In: Rocha, A.P., Steels, L., van den Herik, H.J. (eds.) Proceedings of the 12th International Conference on Agents and Artificial Intelligence, ICAART 2020, vol. 2, pp. 79–88. SCITEPRESS (2020)

    Google Scholar 

  30. Virseda, J., Borrajo, D., Alcázar, V.: Learning heuristic functions for cost-based planning. Planning and Learning, p. 6 (2013)

    Google Scholar 

  31. Yoon, S., Fern, A., Givan, R.: Learning control knowledge for forward search planning. J. Mach. Learn. Res. 9(Apr), 683–718 (2008)

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

Research is supported by the Czech Science Foundation project P103-18-07252S.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic

    Otakar Trunda & Roman Barták

Authors
  1. Otakar Trunda
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  2. Roman Barták
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Corresponding author

Correspondence to Otakar Trunda .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Trunda, O., Barták, R. (2021). Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-71158-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71157-3

  • Online ISBN: 978-3-030-71158-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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