Combining Local Search and Constraint Propagation to find a minimal change solution for a Dynamic CSP

N. Roos*, Y.P. Ran, H.J. van den Herik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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 hin- drance, 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.
Original languageEnglish
Title of host publicationArtificial Intelligence: Methodology, Systems, and Applications, AIMSA 2000
PublisherSpringer, Berlin, Heidelberg
Number of pages11
ISBN (Electronic)978-3-540-45331-4
ISBN (Print)978-3-540-41044-7
Publication statusPublished - 2000

Publication series

SeriesLecture Notes in Artificial Intelligence


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