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In Constraint Programming, selection of a variable and a value of its domain enumeration strategies are crucial for resolution performances. We propose to use a Choice Function for guiding enumeration: we exploit search process features to dynamically adapt a Constraint Programming solver in order to more efficiently solve Constraint Satisfaction Problems. The Choice Function provides guidance to...
In this work we propose a Choice Function for guiding Constraint Programming in the resolution of Constraint Satisfaction Problems. We exploit some search process features to select on the fly the Enumeration Strategy (Variable + Value Selection Heuristics) in order to more efficiently solve the problem at hand. The main novelty of our approach is that we reconfigure the search based solely on performance...
Constraint Programming is a powerful paradigm for solving Combinatorial Problems. In this solver approach, Enumeration Strategies are crucial for resolution performances. In a previous work, we proposed a framework to reactively change strategies showing bad performances, and to use metabacktracks to restore better states when bad decisions were made. In this paper, we design and evaluate strategies...
In constraint programming, enumeration strategies are crucial for resolution performances. In this work, we model the known NP-complete problems Latin Square, Magic Square and Sudoku as a constraint satisfaction problems. We solve them with constraint programming comparing the performance of different variable and value selection heuristics in its enumeration phase.
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