By Justyna Petke
This booklet presents an important step in the direction of bridging the components of Boolean satisfiability and constraint pride by way of answering the query why SAT-solvers are effective on yes periods of CSP situations that are challenging to unravel for traditional constraint solvers. the writer additionally offers theoretical purposes for selecting a specific SAT encoding for a number of vital sessions of CSP instances.
Boolean satisfiability and constraint pride emerged independently as new fields of laptop technological know-how, and varied fixing thoughts became usual for challenge fixing within the parts. even if any propositional formulation (SAT) could be seen to illustrate of the final constraint pride challenge (CSP), the results of this connection have in basic terms been studied within the previous couple of years.
The booklet should be priceless for researchers and graduate scholars in synthetic intelligence and theoretical desktop technological know-how.
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This ebook presents an important step in the direction of bridging the components of Boolean satisfiability and constraint pride via answering the query why SAT-solvers are effective on yes periods of CSP circumstances that are demanding to unravel for normal constraint solvers. the writer additionally provides theoretical purposes for selecting a specific SAT encoding for numerous vital sessions of CSP circumstances.
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Extra resources for Bridging Constraint Satisfaction and Boolean Satisfiability
One reason is that CSP contains a broad class of problems which includes SAT. CSPs are closer in their description to real-world problems. Moreover, most constraint solvers can be tuned, that is the user can choose a specific variable ordering or search strategy. SAT-solvers, on the other hand, typically act as a black box. This difference might have come about as there is no standardised input for CSP-solvers and they can model a broader set of problems than SAT. Hence some CSP solving algorithms take specific problem features into account.
How can suitable benchmark instances be obtained? One obvious source of benchmark instances is from practical applications such as scheduling and manufacturing process organisation; the G12 MiniZinc suite includes several examples of this kind, such as “nurse scheduling” problems and “car sequencing” problems. Another common source of benchmark instances is combinatorial problems such as puzzles and games; the G12 MiniZinc suite also includes several examples of this kind, such as “Golomb ruler” problems and “kakuro” puzzles.
However, the efficient algorithm for CSP instances with bounded-width structures is based on choosing an appropriate variable ordering, and imposing a level of consistency proportional to the width [DKV02, DP89, Fre90]. None of the standard CSP-solvers incorporate this algorithm, so it is not at all evident whether they can solve bounded-width instances efficiently. To investigate this question we wrote a generator for a family of specific CSP instances with a very simple bounded-width structure.