Skip to main content

Divide-And-Evolve Facing State-of-the-Art Temporal Planners during the 6th International Planning Competition

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5482))

Abstract

Divide-and-Evolve (DAE) is the first evolutionary planner that has entered the biennial International Planning Competition (IPC). Though the overall results were disappointing, a detailed investigation demonstrates that in spite of a harsh time constraint imposed by the competition rules, DAE was able to obtain the best quality results in a number of instances. Moreover, those results can be further improved by removing the time constraint, and correcting a problem due to completely random individuals. Room for further improvements are also explored.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bibai, J., Schoenauer, M., Savéant, P., Vidal, V.: DAE: Planning as Artificial Evolution (Deterministic part). In: IPC 2008 Competition Booklet (2008)

    Google Scholar 

  2. Bibai, J., Schoenauer, M., Savéant, P., Vidal, V.: Evolutionary Planification by decomposition. INRIA Research Report No. RT-0355 (2008), http://hal.inria.fr/inria-00322880/en/

  3. Chen, Y., Hsu, C., Wah, B.: Temporal Planning using Subgoal Partitioning and Resolution in SGPlan. Artificial Intelligence 26, 323–369 (2006)

    MATH  Google Scholar 

  4. Fikes, R., Nilsson, N.: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence 2(3-4), 189–208 (1971)

    Article  MATH  Google Scholar 

  5. Fox, M., Long, D.: PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research 20 (2003)

    Google Scholar 

  6. Gerevini, A., Saetti, A., Serina, I.: Planning through Stochastic Local Search and Temporal Action Graphs in LPG. Journal of Artificial Intelligence Research 20, 239–290 (2003)

    MATH  Google Scholar 

  7. Gerevini, A., Saetti, A., Serina, I.: On Managing Temporal Information for Handling Durative Actions. In: LPG. AI*IA 2003: Advances in Artificial Intelligence. Springer, Heidelberg (2003)

    Google Scholar 

  8. Long, D., Fox, M.: Exploiting a Graphplan Framework in Temporal Planning. In: Proceedings of ICAPS 2003, pp. 51–62 (2003)

    Google Scholar 

  9. Mansanne, F., Carrre, F., Ehinger, A., Schoenauer, M.: Evolutionary Algorithms as Fitness Function Debuggers. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  10. McDermott, D.: PDDL – The Planning Domain Definition Language (1998), http://ftp.cs.yale.edu/pub/mcdermott

  11. Schoenauer, M., Savéant, P., Vidal, V.: Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 247–260. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Schoenauer, M., Savéant, P., Vidal, V.: Divide-and-Evolve: a Sequential Hybridisation Strategy using Evolutionary Algorithms. In: Michalewicz, Z., Siarry, P. (eds.) Advances in Metaheuristics for Hard Optimisation, pp. 179–198. Springer, Heidelberg (2007)

    Google Scholar 

  13. Smith, D., Weld, D.S.: Temporal Planning with Mutual Exclusion Reasoning. In: Proc. IJCAI 1999, pp. 326–337 (1999)

    Google Scholar 

  14. Vidal, V., Geffner, H.: Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. In: Proc. of AAAI 2004, pp. 570–577 (2004)

    Google Scholar 

  15. Vidal, V., Geffner, H.: Branching and Pruning: Optimal Temporal POCL Planner based on Constraint Programming. Artificial Intelligence 170(3), 298–335 (2006)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bibai, J., Schoenauer, M., Savéant, P. (2009). Divide-And-Evolve Facing State-of-the-Art Temporal Planners during the 6th International Planning Competition. In: Cotta, C., Cowling, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2009. Lecture Notes in Computer Science, vol 5482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01009-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01009-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01008-8

  • Online ISBN: 978-3-642-01009-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics