Combining Local Search and Constraint Propagation to Find a Minimal Change Solution for a Dynamic CSP

  • Authors:

  • Nico Roos;Yongping Ran;H. Jaap van den Herik

  • Affiliations:

  • -;-;-

  • Venue:
  • AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
  • Year:
  • 2000

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Abstract

Many hard practical problems such as Time Tabling and Scheduling can be formulated as Constraint Satisfaction Problems. For these CSPs, powerful problem-solving methods are available. However, in practice, the problem definition may change over time. Each separate change may invoke a new CSP formulation. The resulting sequence of CSPs is denoted as a Dynamic CSP. A valid solution of one CSP in the sequence need not be a solution of the next CSP. Hence it might be necessary to solve every CSP in the sequence forming a DCSP. Successive solutions of the sequence of CSPs can differ quite considerably. In practical environments large differences between successive solutions are often undesirable. To cope with this hindrance, the paper proposes a repair-based algorithm, i.e., a Local Search algorithm that systematically searches the neighborhood of an infringed solution to find a new nearby solution. The algorithm combines local search with constraint propagation to reduce its time complexity.