PIPSS*: A System based on Temporal Estimates

Conference paper


AI planning and scheduling are two closely related areas. Planning provides a set of actions that achieves a set of goals, and scheduling assigns time and resources to the actions. Currently, most of the real world problems require the use of shared and limited resources with time constraints when planning. Then, systems that can integrate planning and scheduling techniques to deal with this kind of problems are needed.

This paper describes the extension performed in PIPSS (Parallel Integration Planning and Scheduling System) called PIPSS*. PIPSS combines traditional state space heuristic search planning with constraint satisfaction algorithms. The extension is based on heuristic functions that allows the planner to reduce the search space based on time estimations that imposes temporal constraints to the scheduler. The purpose is to achieve a tighter integration respect to the previous version and minimize the makespan. Results show that PIPSS* outperforms state of the art planners under the temporal satisficing track in the IPC-08 competition for the tested domains.


Planning Graph Schedule System Heuristic Function Temporal Network Schedule Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work has been founded by the Junta de Comunidades de Castilla-La Mancha project PEII09-0266-6640.


  1. 1.
    M. D. R-Moreno and D. Camacho and A. Moreno. HPP: A Heuristic Progressive Planner. The 24th Annual Workshop of the UK Planning and Scheduling Special Interest Group (PLANSIG-05). pp: 8-18, London, UK, December, 2005.Google Scholar
  2. 2.
    A. Blum and M. Furst. Fast Planning Through Planning Graph Analysis. Artificial Intelli- gence, vol. 90, pp: 281-300, 1997.MATHCrossRefGoogle Scholar
  3. 3.
    B. Bonet and H. Geffner. Planning as Heuristic Search. Artificial Intelligence, vol. 129, pp: 5-33, 2001.MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    A. Cesta and G. Cortellessa and A. Oddi and N. Policella and A. Susi. A Constraint-Based Architecture for Flexible Support to Activity Scheduling. In Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence, pp: 369-381, Bari, Italy, 2001.Google Scholar
  5. 5.
    J. Plaza and M. D. R-Moreno and B. Castano and M. Carbajo and A. Moreno. PIPSS: Parallel Integrated Planning and Scheduling System. The 27th Annual Workshop of the UK Planning and Schedulinmg Special Interest Group (PLANSIG-08). Edinburgh, UK, December, 2008.Google Scholar
  6. 6.
    A. Cesta and A. Oddi and S. F. Smith. An Iterative Sampling Procedure for Resource Cons- trained Project Scheduling with TimeWindows. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99). Stockholm, Sweden, 1999.Google Scholar
  7. 7.
    C. W. Hsu and B. W. Wah. The SGPlan Planning System in IPC-6. International Planning Competition 6. Corvallis, OR, USA, 2008.Google Scholar
  8. 8.
    V. Vidal and H. Geffner. Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. Artificial Intelligence, vol. 170 (3), pp: 298-335, 2006.MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    J. Bibaї and P. Savéant and M. Schoenauer and V. Vidal. DAE: Planning as Artificial Evolu- tion (Deterministic part). International Planning Competition 6. Corvallis, OR, USA, 2008.Google Scholar
  10. 10.
    A. Gerevini and D. Long. Plan Constraints and Preferences in PDDL3. The Language of the Fifth International Planning Competition. Technical Report, Department of Electronics for Automation, University of Brescia, Italy. 2005.Google Scholar
  11. 11.
    M. Fox and D. Long. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. University of Durham, February, Durham, UK, 2002.Google Scholar
  12. 12.
    R. Cervoni and A. Cesta and A. Oddi. Managing Dynamic Temporal Constraint Networks. In Proceedings of the 2nd International Conference on Artificial Intelligence Planning Systems (AIPS-94). Chicago, USA, 1994.Google Scholar
  13. 13.
    J. Hoffmann and B. Nebel. The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, pp: 253-302, 2001.MATHGoogle Scholar
  14. 14.
    J. Hoffmann. The Metric-FF Planning System: Translating ’Ignoring Delete Lists’ to Numeric State Variables. Journal of Artificial Intelligence Research, vol. 20, pp: 291-341, 2003.MATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Departamento de AutomáticaUniversidad de AlcaláMadridSpain

Personalised recommendations