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T.D. Friederich
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Evaluating Logic Programming in PROLOG for solving Hitori puzzles
A comparison of competing modeling-and-solving paradigms
We evaluate the performance of Logic Programming in PROLOG for solving Hitori puzzles. We analyze the runtime of our model as puzzle size increases, and compare performance to models from
competing modeling-and-solving paradigms. We discuss our model approach, and show that incorporating redundant constraints into our model can reduce average runtime. We discuss PROLOG’s backtracking depth first search and compare it with
techniques used by SAT solvers and CLASP, and briefly discuss options to incorporate some of these search techniques in PROLOG to potentially improve its performance in problems with large search spaces. In the comparison with other paradigms,
Answer Set Programming and Constraint Programming greatly outperformed PROLOG. ...
competing modeling-and-solving paradigms. We discuss our model approach, and show that incorporating redundant constraints into our model can reduce average runtime. We discuss PROLOG’s backtracking depth first search and compare it with
techniques used by SAT solvers and CLASP, and briefly discuss options to incorporate some of these search techniques in PROLOG to potentially improve its performance in problems with large search spaces. In the comparison with other paradigms,
Answer Set Programming and Constraint Programming greatly outperformed PROLOG. ...
We evaluate the performance of Logic Programming in PROLOG for solving Hitori puzzles. We analyze the runtime of our model as puzzle size increases, and compare performance to models from
competing modeling-and-solving paradigms. We discuss our model approach, and show that incorporating redundant constraints into our model can reduce average runtime. We discuss PROLOG’s backtracking depth first search and compare it with
techniques used by SAT solvers and CLASP, and briefly discuss options to incorporate some of these search techniques in PROLOG to potentially improve its performance in problems with large search spaces. In the comparison with other paradigms,
Answer Set Programming and Constraint Programming greatly outperformed PROLOG.
competing modeling-and-solving paradigms. We discuss our model approach, and show that incorporating redundant constraints into our model can reduce average runtime. We discuss PROLOG’s backtracking depth first search and compare it with
techniques used by SAT solvers and CLASP, and briefly discuss options to incorporate some of these search techniques in PROLOG to potentially improve its performance in problems with large search spaces. In the comparison with other paradigms,
Answer Set Programming and Constraint Programming greatly outperformed PROLOG.