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Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions

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Abstract

In this paper we present an approach to avoid dead-ends during automated plan generation. A first-order logic formula can be learned that holds in a state if the application of a specific action will lead to a dead-end. Starting from small problems within a problem domain examples of states where the application of the action will lead to a dead-end will be collected. The states will be generalized using inductive logic programming to a first-order logic formula. We will show how different notions of goal-dependence could be integrated in this approach. The formula learned will be used to speed-up automated plan generation. Furthermore, it provides insight into the planning domain under consideration.

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Notes

  1. We consider problems as small if their number of states is low.

  2. The domain specification may be augmented with a set of constants. Those constants represent objects which are present in every problem of this domain. Since there are ILP algorithms (e. g. FOIL) that can induce formulas with specific objects, this augmentation poses no problem to our approach.

References

  1. Bacchus F, Kabanza F (2000) Using temporal logics to express search control knowledge for planning. Artif Intell 116(12):123–191

    Article  MATH  MathSciNet  Google Scholar 

  2. Estlin TA (1998) Using multi-strategy learning to improve planning efficiency and quality. PhD thesis, Artificial Intelligence Laboratory, The University of Texas at Austin

  3. Fikes RE, Nilsson NJ (1971) Strips: A new approach to the application of theorem proving to problem solving. Artif Intell 2(3–4):189–208

    Article  MATH  Google Scholar 

  4. Hoffmann J, Porteous J, Sebastia L (2004) Ordered landmarks in planning. J Artif Intell Res 22:215–278

    MATH  MathSciNet  Google Scholar 

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

    Article  MATH  Google Scholar 

  6. Minton S (1988) Learning effective search control knowledge: an explanation-based approach. Kluwer Academic Publishers

  7. Minton S, Knoblock C, Kuokka D, Gil Y, Joseph R, Carbonell J (1989) Prodigy2.0: the manual and tutorial. Tech Rep, Carnegie Mellon

  8. Muggleton S, Feng C (1992) Efficient induction in logic programs. In: Muggleton S (ed) Inductive logic programming, Academic Press, pp 281–298

  9. Quinlan J (1990) Learning logical definitions from relations. Mach Learn 5(3):239–266

    Google Scholar 

  10. de la Rosa T, McIlraith SA (2011) Learning domain control knowledge for TLPlan and beyond. In: Proceedings of the ICAPS-11 workshop on planning and learning (PAL)

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Authors and Affiliations

  1. Cognitive Systems Group, Faculty Information Systems and Applied Computer Sciences, University of Bamberg, Bamberg, Germany

    Michael Siebers

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  1. Michael Siebers
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Correspondence to Michael Siebers.

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Siebers, M. Transfer of Domain Knowledge in Plan Generation: Learning Goal-dependent Annulling Conditions for Actions. Künstl Intell 28, 35–38 (2014). https://doi.org/10.1007/s13218-013-0282-z

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