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Goal distance estimation for automated planning using neural networks and support vector machines

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Abstract

Many of today’s most successful planners perform a forward heuristic search. The accuracy of the heuristic estimates and the cost of their computation determine the performance of the planner. Thanks to the efforts of researchers in the area of heuristic search planning, modern algorithms are able to generate high-quality estimates. In this paper we propose to learn heuristic functions using artificial neural networks and support vector machines. This approach can be used to learn standalone heuristic functions but also to improve standard planning heuristics. One of the most famous and successful variants for heuristic search planning is used by the Fast-Forward (FF) planner. We analyze the performance of standalone learned heuristics based on nature-inspired machine learning techniques and employ a comparison to the standard FF heuristic and other heuristic learning approaches. In the conducted experiments artificial neural networks and support vector machines were able to produce standalone heuristics of superior accuracy. Also, the resulting heuristics are computationally much more performant than related ones.

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Notes

  1. http://www2.parc.com/isl/members/syoon/obtusewedge.html.

References

  • Blum A, Furst M (1995) Fast planning through planning graph analysis. In: IJCAI’95: proceedings of the 14th international joint conference on artificial intelligence, pp 1636–1642

  • Bonet B, Geffner H (2000) HSP: heuristic search planner. Entry at AIPS-98 planning competition. AI Mag 21(2)

  • Bonet B, Geffner H (2001) Planning as heuristic search. Artif Intell 129(1–2):5–33

    Article  MathSciNet  MATH  Google Scholar 

  • Botea A, Enzenberger M, Müller M, Schaeffer J (2005) Macro-FF: improving AI planning with automatically learned macro-operators. J Artif Intell Res 24(1):581–621

    MATH  Google Scholar 

  • Bylander T (1994) The computational complexity of propositional STRIPS planning. Artif Intell 69:165–204

    Article  MathSciNet  MATH  Google Scholar 

  • Coles A, Smith KA (2007) Marvin: a heuristic search planner with online macro-action learning. J Artif Intell Res 28:119–156

    MATH  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  • Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik V (1996) Support vector regression machines. In: Mozer M, Jordan MI, Petsche T (eds) NIPS. MIT Press, Cambridge, pp 155–161

    Google Scholar 

  • Ebendt R, Drechsler R (2009) Weighted A* search—unifying view and application. Artif Intell 173:1310–1342

    Article  MathSciNet  MATH  Google Scholar 

  • Fern A, Khardon R, Tadepalli P (2011) The first learning track of the international planning competition. Mach Learn 84:81–107

    Article  Google Scholar 

  • Fikes R, Nilsson NJ (1971) STRIPS: a new approach to the application of theorem proving to problem solving. Artif Intell 2(4):189–208

    Article  MATH  Google Scholar 

  • Frank J (2007) Using data mining to enhance automated planning and scheduling. In: Proceedings of the IEEE symposium on computational intelligence and data mining. IEEE, pp 251–260

  • Ghallab M, Howe A, Knoblock C, McDermott D, Ram A, Veloso M, Weld D, Wilkins D (1998) PDDL—the planning domain definition language. Technical report. Yale Center for Computational Vision and Control

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18

    Google Scholar 

  • Helmert M (2006) The fast downward planning system. J Artif Intell Res 26:191–246

    Article  MATH  Google Scholar 

  • Helmert M, Domshlak C (2009) Landmarks, critical paths and abstractions: what’s the difference anyway? In: ICAPS’09: proceedings of the 19th international conference on automated planning and scheduling. AAAI

  • Hoffmann J (2001) FF: the fast-forward planning system. AI Mag 22:57–62

    Google Scholar 

  • Nilsson NJ (1982) Principles of artificial intelligence. Springer, Berlin

    MATH  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. In: Neurocomputing: foundations of research, pp 696–699. http://www.nature.com/nature/journal/v323/n6088/abs/323533a0.html

  • Satzger B, Kramer O (2010) Learning heuristic functions for state-space planning. In: CI’10: proceedings of the 5th international conference on computational intelligence, pp 36–43

  • Satzger B, Kramer O, Lässig J (2010) Adaptive heuristic estimates for automated planning using regression. In: International conference on artificial intelligence, pp 576–581

  • Satzger B, Pietzowski A, Trumler W, Ungerer T (2008) Using automated planning for trusted self-organising organic computing systems. In: ATC’08: proceedings of the 5th international conference on autonomic and trusted computing. Springer, pp 60–72

  • Shevade S, Keerthi S, Bhattacharyya C, Murthy K (1999) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Networks. doi:10.1109/72.870050

  • Swiercz M, Kochanowicz J, Weigele J, Hurst R, Liebeskind D, Mariak Z, Melhem E, Krejza J (2008) Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm—preliminary report. Neuroinformatics 6:279–290

    Article  Google Scholar 

  • Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  • Widrow B, Hoff ME (1960) Adaptive switching circuits. IRE WESCON Conv Record 4:96–104

    Google Scholar 

  • Xu Y, Fern A, Yoon S (2009) Learning weighted rule sets for forward search planning. In: Workshop on planning and learning, ICAPS-2009

  • Yoon SW, Fern A, Givan R (2006) Learning heuristic functions from relaxed plans. In: ICAPS’06: proceedings of the 16th international conference on automated planning and scheduling. AAAI, pp 162–171

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

    MathSciNet  MATH  Google Scholar 

  • Zimmerman T, Kambhampati S (2003) Learning-assisted automated planning: looking back, taking stock, going forward. AI Mag 24(2):73–96

    Google Scholar 

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Acknowledgments

This work was supported by a fellowship within the Postdoc-Program of the German Academic Exchange Service (DAAD).

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Authors and Affiliations

  1. Distributed Systems Group, Vienna University of Technology, Vienna, Austria

    Benjamin Satzger

  2. Computational Intelligence Group, University of Oldenburg, Oldenburg, Germany

    Oliver Kramer

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  1. Benjamin Satzger
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  2. Oliver Kramer
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Correspondence to Benjamin Satzger.

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Satzger, B., Kramer, O. Goal distance estimation for automated planning using neural networks and support vector machines. Nat Comput 12, 87–100 (2013). https://doi.org/10.1007/s11047-012-9332-y

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