Nothing Special   »   [go: up one dir, main page]

Skip to main content

Towards a New Generation ACO-Based Planner

  • Conference paper
Computational Science and Its Applications – ICCSA 2014 (ICCSA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8584))

Included in the following conference series:

Abstract

In this paper a new generation ACO-Based Planner, called ACOPlan 2013, is described. This planner is an enhanced version of ACOPlan, a previous ACO-Based Planner [3], which differs from the former in the search algorithm and in the implementation, now done on top of Downwards. The experimental results, even if are not impressive, are encouraging and confirm that ACO is a suitable method to find near optimal plan for propositional planning problems.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: An ACO approach to planning. In: Cotta, C., Cowling, P. (eds.) EvoCOP 2009. LNCS, vol. 5482, pp. 73–84. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: Ant search strategies for planning optimization. In: Proc of the International Conference on Planning and Scheduling, ICAPS 2009 (2009)

    Google Scholar 

  3. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: Experimental evaluation of pheromone models in acoplan. Ann. Math. Artif. Intell. 62(3-4), 187–217 (2011)

    Article  MATH  Google Scholar 

  4. Blum, C.: Ant colony optimization: Introduction and recent trends. Physics of Life Reviews 2(4), 353–373 (2005)

    Article  Google Scholar 

  5. Do, M.B., Kambhampati, S.: Sapa: A multi-objective metric temporal planner. Journal of Artificial Intelligence Research (JAIR) 20, 155–194 (2003)

    MATH  Google Scholar 

  6. Dorigo, M., Stuetzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  7. Fawcett, C., Helmert, M., Hoos, H., Karpas, E., Roeger, G., Seipp, J.: Fd-autotune: Domain-specific configuration using fast downward. In: ICAPS 2011, PAL Workshop, Runner-up in Learning Track, IPC (2011)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  9. Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine. Community of scientist optimization: An autonomy oriented approach to distributed optimization. ACM TIST 3(3) (2012)

    Google Scholar 

  10. Keyder, E., Geffner, H.: Heuristics for planning with action costs revisited. In: Proc. of ECAI 2008, pp. 588–592 (2008)

    Google Scholar 

  11. Cialdea, M., Limongelli, C., Poggioni, V., Orlandini, A.: Linear temporal logic as an executable semantics for planning languages. Journal of logic, language and information 16, 63–89 (2007)

    MATH  MathSciNet  Google Scholar 

  12. Milani, A., Santucci, V.: Community of scientist optimization: An autonomy oriented approach to distributed optimization. AI Commun. 25(2) (2012)

    Google Scholar 

  13. Richter, S., Westphal, M.: The lama planner: Guiding cost-based anytime planning with landmarks. J. Artif. Intell. Res (JAIR) 39, 127–177 (2010)

    MATH  Google Scholar 

  14. Tasso, S., Pallottelli, S., Ciavi, G., Bastianini, R., Laganà, A.: An efficient taxonomy assistant for a federation of science distributed repositories: A chemistry use case. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part I. LNCS, vol. 7971, pp. 96–109. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Milani, J.L.A., Cheng, V.C., Leung, C.H.C.: Probabilistic aspect mining model for drug reviews. IEEE Transactions on Knowledge and Data Engineering 99 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Baioletti, M., Chiancone, A., Poggioni, V., Santucci, V. (2014). Towards a New Generation ACO-Based Planner. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-09153-2_59

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09152-5

  • Online ISBN: 978-3-319-09153-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics