Learning and Solving Soft Temporal Constraints: An Experimental Study

  • Francesca Rossi
  • Alessandro Sperduti
  • Kristen B. Venable
  • Lina Khatib
  • Paul Morris
  • Robert Morris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2470)


Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem, and then to learn from them suitable preferences over distances and durations.

In this paper, we describe our learning algorithm and we show its behaviour on classes of randomly generated problems. Moreover, we also describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we give experimental results to compare them.


Preference Function Local Preference Constraint Satisfaction Problem Temporal Constraint Soft Constraint 
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.


  1. [1]
    A. Biso, F. Rossi, and A. Sperduti. Experimental Results on Learning Soft Constraints. Proc. KR 2000, Morgan Kaufmann, 2000.Google Scholar
  2. [2]
    Abstracting Soft Constraints. S. Bistarelli, P. Codognet, Y. Georget, F. Rossi. Proc. ERCIM/Compulog Net work. on constraints, Springer, LNAI 1865, 2000.Google Scholar
  3. [3]
    S. Bistarelli, U. Montanari, and F. Rossi. Semiring-based Constraint Solving and Optimization. Journal of the A CM, 44(2):201–236, March 1997.Google Scholar
  4. [4]
    R. Dechter, I. Meiri, and J. Pearl. Temporal constraint networks. Artificial Intelligence, Vol. 49, 1991, pp. 61–95.MathSciNetzbMATHCrossRefGoogle Scholar
  5. [5]
    D. Dubois, H. Prade. Processing Fuzzy Temporal Knowledge. IEEE Trans. On systems, Man, and Cybernetics, 19:4, 1989.MathSciNetCrossRefGoogle Scholar
  6. [6]
    M. Giacomin. From Crisp to Fuzzy Constraints Networks. Proc. CP’ 01 Workshop on Modelling and Solving Problems with Soft Constraints, 2001.Google Scholar
  7. [7]
    S. Haykin. Neural Networks: a comprehensive Foundation. IEEE Press, 1994.Google Scholar
  8. [8]
    L. Khatib, P. Morris, R. Morris, F. Rossi. Temporal Constraint Reasoning With Preferences. Proc. IJCAI 2001.Google Scholar
  9. [9]
    L. Khatib, P. Morris, R. Morris, F. Rossi, A. Sperduti. Learning Preferences on Temporal Constraints: A Preliminary Report. Proc. TIME 2001, IEEE Comp. Soc. Press, 2001.Google Scholar
  10. [10]
    R. Marin, M. A. Cardenas, M. Balsa, J. L. Sanchez. Obtaining solutions in Fuzzy Constraint Networks. Int. Jour. of Approximate Reasoning, 16:261–288, 1997.MathSciNetzbMATHCrossRefGoogle Scholar
  11. [11]
    F. Rossi and A. Sperduti. Learning solution preferences in constraint problems. Journal of Experimental and Theoretical Computer Science, 1998. Vol 10.Google Scholar
  12. [12]
    T. Schiex. Possibilistic constraint satisfaction problems, or “how to handle soft constraints?”. Proc. 8th Conf. of Uncertainty in AI, pages 269–275, 1992.Google Scholar
  13. [13]
    E. Schwalb, R. Dechter. Coping with disjunctions in temporal constraint satisfaction problems. Proc. AAAI-93, 1993.Google Scholar
  14. [14]
    S. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 1995.Google Scholar
  15. [15]
    L. Vila, L. Godo. On fuzzy temporal constraint networks. Mathware and Soft Computing, 3:315–334, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Francesca Rossi
    • 1
  • Alessandro Sperduti
    • 1
  • Kristen B. Venable
    • 1
  • Lina Khatib
    • 2
    • 3
  • Paul Morris
    • 2
  • Robert Morris
    • 2
  1. 1.Department of Pure and Applied MathematicsUniversity of PadovaItaly
  2. 2.NASA Ames Research CenterMoffett FieldUSA
  3. 3.Kestrel TechnologyUSA

Personalised recommendations