The Benefit of Control Knowledge and Heuristics During Search in Planning

Jindřich Vodrážka, Roman Barták

Abstract

The overall performance of classical planner depends heavily on the domain model which can be enhanced by adding control knowledge and heuristics. Both of them are known techniques which can boost the search process in exchange for some computational overhead needed for their repeated evaluation. Our experiments show that the gain from usage of heuristics and control knowledge is evolving throughout the search process and also depends on the type of search algorithm. We demonstrate the idea using the branch-and-bound and iterative deepening search techniques, both implemented in the Picat planning module.

References

  1. Bacchus, F. and Kabanza, F. (1999). Using temporal logics to express search control knowledge for planning. Artificial Intelligence, 116:2000.
  2. Baier, J., Fritz, C., and McIlraith, S. A. (2007). Exploiting procedural domain control knowledge in state-of-theart planners. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS), pages 26-33.
  3. Barták, R. and Vodráz?ka, J. (2015). Searching for sequential plans using tabled logic programming. In 22nd RCRA International Workshop on Experimental Evaluation of Algorithms for solving problems with combinatorial explosion.
  4. Barták, R. and Vodráz?ka, J. (2016). The effect of domain modeling on the performance of planning algorithms. In International Symposium on Artificial Intelligence and Mathematics (ISAIM).
  5. Bonet, B. and Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence, 129:5-33.
  6. Doig, A. G., Land, B. H., and Doig, A. G. (1960). An automatic method for solving discrete programming problems. Econometrica, pages 497-520.
  7. Ghallab, M., Nau, D., and Traverso, P. (2004). Automated Planning: Theory & Practice. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  8. Haslum, P. and Scholz, U. (2003). Domain knowledge in planning: Representation and use. In Proc. ICAPS 2003 Workshop on PDDL.
  9. Helmert, M., Rger, G., Seipp, J., Karpas, E., Hoffmann, J., Keyder, E., Nissim, R., Richter, S., and Westphal, M. (2011). Fast downward stone soup. Available at: http://www.fast-downward.org/IpcPlanners.
  10. Hoffmann, J. and Nebel, B. (2001). The ff planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14(1):253-302.
  11. Kautz, H. and Selman, B. (1992). Planning as satisfiability. In ECAI-92, pages 359-363. Wiley.
  12. Kissmann, P., Edelkamp, S., and Hoffmann, J. (2014). Gamer and dynamic-gamer symbolic search at ipc 2014. Available at: https://fai.cs.unisaarland.de/kissmann/planning/downloads/.
  13. Korf, R. E. (1985). Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence, 27:97-109.
  14. Kvarnström, J. and Magnusson, M. (2003). Talplanner in the third international planning competition: Extensions and control rules. Journal of Artificial Intelligence Research, 20:343-377.
  15. McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., and Wilkins, D. (1998). PDDL - the planning domain definition language. Technical report, CVC TR-98-003/DCS TR1165, Yale Center for Computational Vision and Control.
  16. Nau, D., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., and Yaman, F. (2003). Shop2: An htn planning system. Journal of Artificial Intelligence Research, 20:379-404.
  17. Tange, O. (2011). Gnu parallel - the command-line power tool. ;login: The USENIX Magazine, 36(1):42-47.
  18. Zhou, N. F. (2015). Picat web site. http://picat-lang.org/. Accessed October 18.
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Paper Citation


in Harvard Style

Vodrážka J. and Barták R. (2016). The Benefit of Control Knowledge and Heuristics During Search in Planning . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 552-559. DOI: 10.5220/0005828005520559


in Bibtex Style

@conference{icaart16,
author={Jindřich Vodrážka and Roman Barták},
title={The Benefit of Control Knowledge and Heuristics During Search in Planning},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={552-559},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005828005520559},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - The Benefit of Control Knowledge and Heuristics During Search in Planning
SN - 978-989-758-172-4
AU - Vodrážka J.
AU - Barták R.
PY - 2016
SP - 552
EP - 559
DO - 10.5220/0005828005520559