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GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables

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Recent Advances in AI Planning (ECP 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1809))

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Abstract

This paper presents Greedy Regression Tables (GRT), a new domain independent heuristic for STRIPS worlds. The heuristic can be used to guide the search process of any state-space planner, estimating the distance between each intermediate state and the goals. At the beginning of the problem solving process a table is created, the records of which contain the ground facts of the domain, among with estimates for their distances from the goals. Additionally, the records contain information about interactions that occur while trying to achieve different ground facts simultaneously. During the search process, the heuristic, using this table, extracts quite accurate estimates for the distances between intermediate states and the goals. A simple best-first search planner that uses this heuristic has been implemented in C++ and has been tested on several “classical” problem instances taken from the bibliography and on some new taken from the AIPS-98 planning competition. Our planner has proved to be faster in all of the cases, finding also in most (but not all) of the cases shorter solutions.

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References

  1. Barett, A., Weld, D.S.: Partial order planning: Evaluating possible efficiency gains. J. Artificial Intelligence 67, 71–112 (1994)

    Article  Google Scholar 

  2. Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), Montreal, Canada, pp. 636–642 (1995)

    Google Scholar 

  3. Bonet, B., Loerincs, G., Geffner, H.: A robust and fast action selection mechanism for planning. In: 14th International Conference of the American Association of Artificial Intelligence (AAAI 1997), Providence, Rhode Island, pp. 714–719 (1997)

    Google Scholar 

  4. Bonet, B., Geffner, H.: HSP: Heuristic Search Planner. Entry at the Artificial Intelligence Planning Systems (AIPS 1998) Planning Competition, Pittsburgh (1998)

    Google Scholar 

  5. Fikes, R.E., Nilsson, N.J.: Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208 (1971)

    Article  MATH  Google Scholar 

  6. Gupta, N., Nau, D.S.: On the complexity of blocks world planning. Artificial Intelligence 56(2-3), 223–254 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  7. Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Systems Science and Cybernetics 4(2), 100–107 (1968)

    Article  Google Scholar 

  8. Kautz, H., Selman, B.: Pushing the envelope: Planning, propositional logic and stochastic search. In: 13th International Conference of the American Association of Artificial Intelligence (AAAI 1996), Portland, Oregon, pp. 1194–1201 (1996)

    Google Scholar 

  9. Kautz, H., Selman, B.: BLACKBOX: A New Approach to the Application of Theorem Proving to Problem Solving. In: Working notes of the Workshop on Planning as Combinatorial Search, held in conjunction with Artificial Intelligence Planning Systems (AIPS 1998), Pittsburgh (1998)

    Google Scholar 

  10. Minton, S., Bresina, J., Drummond, M.: Total-order and partial-order planning: A comparative analysis. J. of Artificial Intelligence Research 2, 227–261 (1994)

    Google Scholar 

  11. Pearl, J.: Heuristics: intelligent strategies for computer problem solving. Addison- Wesley, MA (1984)

    Google Scholar 

  12. Slaney, J., Thiebaux, S.: Linear-time near-optimal planning in the blocks world. In: 13th International Conference of the American Association of Artificial Intelligence (AAAI 1996), Portland, Oregon (1996)

    Google Scholar 

  13. Veloso, M.: Learning by Analogical Reasoning in General Problem Solving. PhD Dissertation, Computer Science Dept., CMU Tech. Report, CMU-CS-92-174 (1992)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 54006, Thessaloniki, Greece

    Ioannis Refanidis & Ioannis Vlahavas

Authors
  1. Ioannis Refanidis
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  2. Ioannis Vlahavas
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Editor information

Editors and Affiliations

  1. Institute for Artificial Intelligence, Ulm University, Germany

    Susanne Biundo

  2.  ,  

    Maria Fox

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© 2000 Springer-Verlag Berlin Heidelberg

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Refanidis, I., Vlahavas, I. (2000). GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables. In: Biundo, S., Fox, M. (eds) Recent Advances in AI Planning. ECP 1999. Lecture Notes in Computer Science(), vol 1809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720246_27

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  • DOI: https://doi.org/10.1007/10720246_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67866-3

  • Online ISBN: 978-3-540-44657-6

  • eBook Packages: Springer Book Archive

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