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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)

Abstract

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.

Keywords

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.

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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

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