Efficient incremental modelling and solving

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In various scenarios, a single phase of modelling and solving is either not sufficient or not feasible to solve the problem at hand. A standard approach to solving AI planning problems, for example, is to incrementally extend the planning horizon and solve the problem of trying to find a plan of a particular length. Indeed, any optimization problem can be solved as a sequence of decision problems in which the objective value is incrementally updated. Another example is constraint dominance programming (CDP), in which search is organized into a sequence of levels. The contribution of this work is to enable a native interaction between SAT solvers and the automated modelling system Savile Row to support efficient incremental modelling and solving. This allows adding new decision variables, posting new constraints and removing existing constraints (via assumptions) between incremental steps. Two additional benefits of the native coupling of modelling and solving are the ability to retain learned information between SAT solver calls and to enable SAT assumptions, further improving flexibility and efficiency. Experiments on one optimisation problem and five pattern mining tasks demonstrate that the native interaction between the modelling system and SAT solver consistently improves performance significantly.
Original languageEnglish
Title of host publicationModRef 2020 - The 19th workshop on Constraint Modelling and Reformulation
Number of pages15
Publication statusPublished - 7 Sept 2020
EventThe 19th workshop on Constraint Modelling and Reformulation (ModRef) - Louvain-la-Neuve, Belgium
Duration: 7 Sept 20207 Sept 2020
Conference number: 19


WorkshopThe 19th workshop on Constraint Modelling and Reformulation (ModRef)
Abbreviated titleModRef
Internet address


  • Constraint programming
  • Constraint modelling
  • Incremental solving
  • Constraint optimization
  • Planning
  • Data mining
  • Itemset mining
  • Pattern mining
  • Dominance programming


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