Authors:
Cleyton Mário de Oliveira Rodrigues
1
;
Eric Rommel Dantas Galvão
1
;
Ryan Ribeiro de Azevedo
1
and
Marcos Aurélio Almeida da Silva
2
Affiliations:
1
Universidade Federal de Pernambuco, Brazil
;
2
Paris Unviversitas, France
Keyword(s):
Constraint Satisfaction Problem, Heuristics, Constraint Logic Programming.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Constraint Satisfaction
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Representation and Reasoning
;
Logic Programming
;
Planning and Scheduling
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
Along the years, many researches in the Artificial Intelligence (AI) field seek for new algorithms to reduce drastically the amount of memory and time consumed for general searches in the family of constraint satisfaction problems. Normally, these improvements are reached with the use of some heuristics which either prune useless tree search branches or even “indicate” the path to reach the solution (many times, the optimal solution) more easily. Many heuristics were proposed in the literature, like Static/ Dynamic Highest Degree heuristic (SHD/DHD), Most Constraint Variable (MCV), Least Constraining Value (LCV), and while some can be used at the pre-processing time, others just at running time. In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First (LSVF). LSVF emerges as a practical solution whenever the LCV can not distinguish how much a value is constrained. We present a pedagogical example
to introduce the heuristics, realized through the general Constraint Logic Programming CHRv, as well as the preliminary results.
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