skip to main content
research-article

A conformant planner based on approximation: CpA(H)

Published:03 April 2013Publication History
Skip Abstract Section

Abstract

This article describes the planner CpA(H), the recipient of the Best Nonobservable Nondeterministic Planner Award in the “Uncertainty Track” of the 6th International Planning Competition (IPC), 2008. The article presents the various techniques that help CpA(H) to achieve the level of performance and scalability exhibited in the competition. The article also presents experimental results comparing CpA(H) with state-of-the-art conformant planners.

References

  1. Albore, A., Ramirez, M., and Geffner, H. 2011. Effective heuristics and belief tracking for planning with incomplete information. In Proceedings of the 21st International Conference on Automated Planning and Scheduling. 2--8.Google ScholarGoogle Scholar
  2. Baral, C., Kreinovich, V., and Trejo, R. 2000. Computational complexity of planning and approximate planning in the presence of incompleteness. Artif. Intell. 122, 241--267. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Bertoli, P., Cimatti, A., and Roveri, M. 2001. Heuristic search + symbolic model checking = efficient conformant planning. In Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), B. Nebel, Ed. Morgan Kaufmann, 467--472. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bertsekas, D. 1995. Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Bonet, B. and Geffner, H. 2001. Planning as heuristic search. Artif. Intell. 129, 1-2, 5--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bonet, B. and Givan, B. 2006. Results of the conformant track of the 5th planning competition. http://www.ldc.usb.ve/~bonet/.Google ScholarGoogle Scholar
  7. Brafman, R. and Hoffmann, J. 2004. Conformant planning via heuristic forward search: A new approach. In Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS'04), S. Koenig, S. Zilberstein, and J. Koehler, Eds. Morgan Kaufmann, 355--364.Google ScholarGoogle Scholar
  8. Bryant, R. E. 1992. Symbolic boolean manipulation with ordered binary decision diagrams. ACM Comput. Surv. 24, 3, 293--318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Bryce, D. and Kambhampati, S. 2004. Heuristic guidance measures for conformant planning. In Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS'04). AAAI, 365--375.Google ScholarGoogle Scholar
  10. Bryce, D., Kambhampati, S., and Smith, D. 2006. Planning graph heuristics for belief space search. J. Artif. Intell. Res. 26, 35--99. Google ScholarGoogle ScholarCross RefCross Ref
  11. Bylander, T. 1994. The computational complexity of propositional strips planning. Artif. Intell. 69, 1-2, 165--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Chapman, D. 1987. Planning for conjunctive goals. Artif. Intell. 32, 3, 333--377. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Cimatti, A., Roveri, M., and Bertoli, P. 2004. Conformant planning via symbolic model checking and heuristic search. Artif. Intell. J. 159, 127--206. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Cushing, W. and Bryce, D. 2005. State agnostic planning graphs and the application to belief-space planning. In Proceedings of the 20th National Conference on Artificial Intelligence and the 17th Innovative Applications of Artificial Intelligence Conference. M. Veloso and S. Kambhampati, Eds., AAAI Press/The MIT Press, 1131--1138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Erol, K., Nau, D., and Subrahmanian, V. 1995. Complexity, decidability and undecidability results for domain-independent planning. Artif. Intell. 76, 1-2, 75--88. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Gerevini, A. and Long, D. 2005. Plan constraints and preferences in PDDL 3.0. Tech. rep., University of Brescia, Italy.Google ScholarGoogle Scholar
  17. Ghallab, M., Howe, A., Knoblock, C., Mcdermott, D., Ram, A., Veloso, M., Weld, D., and Wilkins, D. 1998. PDDL - The planning domain definition language, version 1.2. Tech. rep. CVC TR98003/DCS TR1165, Yale.Google ScholarGoogle Scholar
  18. Hoffmann, J. and Nebel, B. 2001. The ff planning system: Fast plan generation through heuristic search. J. Artif. Intell. Res. 14, 1, 253--302. Google ScholarGoogle ScholarCross RefCross Ref
  19. Hoffmann, J., Porteous, J., and Sebastia, L. 2004. Ordered landmarks in planning. J. Artif. Intell. Res. 22, 215--278. Google ScholarGoogle ScholarCross RefCross Ref
  20. Kaelbling, L., Littman, M., and Cassandra, A. 1998. Planning and acting in partially observable stochastic domains. Artif. Intell. 101, 1--2, 99--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Long, D. and Fox, M. 1999. Efficient implementation of the plan graph in stan. J. Artif. Intell. Res. 10, 87--115. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Nguyen, X., Kambhampati, S., and Nigenda, R. 2002. Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search. Artif. Intell. 135, 1-2, 73--123. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Ong, S. C. W., Png, S. W., Hsu, D., and Lee, W. S. 2010. Planning under uncertainty for robotic tasks with mixed observability. Int. J. Robotic Res. 29, 8, 1053--1068. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Palacios, H. and Geffner, H. 2006. Compiling uncertainty away: Solving conformant planning problems using a classical planner (sometimes). In Proceedings of the 21st National Conference on Artificial Intelligence. 900--905. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Palacios, H. and Geffner, H. 2007. From conformant into classical planning: Efficient translations that may be complete too. In Proceedings of the 17th International Conference on Planning and Scheduling. 264--271.Google ScholarGoogle Scholar
  26. Palacios, H. and Geffner, H. 2009. Compiling uncertainty away in conformant planning problems with bounded width. J. Artif. Intell. Res. 35, 623--675. Google ScholarGoogle ScholarCross RefCross Ref
  27. Petrick, R. P. A. and Bacchus, F. 2002. A knowledge-based approach to planning with incomplete information and sensing. In Proceedings of the 6th International Conference on Artificial Intelligence Planning Systems. AAAI, 212--222.Google ScholarGoogle Scholar
  28. Petrick, R. P. A. and Bacchus, F. 2004. Extending the knowledge-based approach to planning with incomplete information and sensing. In Proceedings of the 6th International Conference on Automated Planning and Scheduling. 2--11.Google ScholarGoogle Scholar
  29. Putterman, M. 1994. Markov Decision Processes - Discrete Stochastic Dynamic Programming. John Willey & Sons, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Shani, G., Poupart, P., Brafman, R. I., and Shimony, S. E. 2008. Efficient add operations for point-based algorithms. In Proceedings of the International Conference on Autonomous Planning and Scheduling. J. Rintanen, B. Nebel, J. C. Beck, and E. A. Hansen, Eds., AAAI, 330--337.Google ScholarGoogle Scholar
  31. Smith, D. and Weld, D. 1998. Conformant graphplan. In Proceedings of the National Conference on Artificial Conference (AAAI). 889--896. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Son, T. C. and Baral, C. 2001. Formalizing sensing actions A transition function based approach. Artif. Intell. 125, 1-2, 19--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Son, T. C. and Tu, P. H. 2006. On the completeness of approximation based reasoning and planning in action theories with incomplete information. In Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning. 481--491.Google ScholarGoogle Scholar
  34. Son, T. C., Tu, P. H., Gelfond, M., and Morales, R. 2005a. An approximation of action theories of al and its application to conformant planning. In Proceedings of the 7th International Conference on Logic Programming and NonMonotonic Reasoning. 172--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Son, T. C., Tu, P. H., Gelfond, M., and Morales, R. 2005b. Conformant planning for domains with constraints - New approach. In Proceedings of the 20th National Conference on Artificial Intelligence. 1211--1216. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Son, T. C., Tu, P. H., Pontelli, E., Tran, D.-V., and Nguyen, H.-K. 2009. Completeness of approximation based reasoning and planning in action theories with incomplete information. Tech. rep.Google ScholarGoogle Scholar
  37. To, S. T., Pontelli, E., and Son, T. C. 2009. A conformant planner with explicit disjunctive representation of belief states. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS'09). A. Gerevini, A. E. Howe, A. Cesta, and I. Refanidis, Eds., AAAI, 305--312.Google ScholarGoogle Scholar
  38. To, S. T., Son, T. C., and Pontelli, E. 2010. A new approach to conformant planning using CNF. In Proceedings of the 20th International Conference on Planning and Scheduling (ICAPS). 169--176.Google ScholarGoogle Scholar
  39. Tran, D.-V., Nguyen, H.-K., Pontelli, E., and Son, T. C. 2009. Improving performance of conformant planners: Static analysis of declarative planning domain specifications. In Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages (PADL). A. Gill and T. Swift, Eds, Lecture Notes in Computer Science, vol. 5418, Springer, 239--253. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Tu, P. H. 2007. Reasoning and planning with incomplete information in the presence of static causal laws. Ph.D. thesis, New Mexico State University.Google ScholarGoogle Scholar
  41. Tu, P. H., Son, T. C., and Baral, C. 2006. Reasoning and planning with sensing actions, incomplete information, and static causal laws using logic programming. Theory Pract. Logic Program. 7, 1--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Tu, P. H., Son, T. C., Gelfond, M., and Morales, R. 2011. Approximation of action theories and its application to conformant planning. Artif. Intell. J. 175, 1, 79--119. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Younes, H. and Littman, M. 2004. PPDDL 1.0: An extension to PDDL for expressing planning domains with probabilistic effects. Tech. rep., Carnegie Mellon University.Google ScholarGoogle Scholar

Index Terms

  1. A conformant planner based on approximation: CpA(H)

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 4, Issue 2
      Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
      March 2013
      339 pages
      ISSN:2157-6904
      EISSN:2157-6912
      DOI:10.1145/2438653
      Issue’s Table of Contents

      Copyright © 2013 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 April 2013
      • Accepted: 1 April 2012
      • Revised: 1 February 2012
      • Received: 1 September 2011
      Published in tist Volume 4, Issue 2

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader