Abstract:
. In the last twenty years, many algorithms and heuristics were developed to find solutions in constraint networks. Their number increased to such an extent that it quickly became necessary to compare their performances in order to propose a small number of "good" methods. These comparisons often led us to consider FC or FC-CBJ associated with a "minimum domain" variable ordering heuristic as the best techniques to solve a wide variety of constraint networks. In this paper, we first try to convince once and for all the CSP community that MAC is not only more efficient than FC to solve large practical problems, but it is also really more efficient than FC on hard and large random problems. Afterwards, we introduce an original and efficient way to combine variable ordering heuristics. Finally, we conjecture that when a good variable ordering heuristic is used, CBJ becomes an expensive gadget which almost always slows down the search, even if it saves a few constraint checks. 1 Introducti...
Citations
|
346
|
Constraint satisfaction in Logic programming
– Hentenryck
- 1989
|
|
341
|
Network-based heuristics for constraint satisfaction problems
– Dechter, Pearl
- 1987
|
|
309
|
Algorithms for Constraint-Satisfaction Problems: A Survey
– Kumar
- 1992
|
|
293
|
Hybrid algorithms for the constraint satisfaction problem
– Prosser
- 1993
|
|
220
|
A sufficient condition for backtrack-free search
– Freuder
- 1982
|
|
186
|
Limited discrepancy search
– Harvey, Ginsberg
- 1995
|
|
162
|
Contradicting conventional wisdom in constraint satisfaction
– Sabin, Freuder
- 1994
|
|
150
|
Performance measurement and analysis of certain search algorithms
– Gaschnig
- 1979
|
|
120
|
Constraint satisfaction algorithms
– Nadel
- 1989
|
|
78
|
Search rearrangement backtracking and polynomial average time
– Purdom
- 1983
|
|
70
|
Dual viewpoint heuristics for binary constraint satisfaction problems
– Geelen
- 1992
|
|
64
|
Binary constraint satisfaction problems: Some are harder than others
– Prosser
- 1994
|
|
59
|
Using inference to reduce arc-consistency computation
– Freuder, Régin, et al.
- 1995
|
|
54
|
Dynamic variable ordering in CSPs
– Bacchus, Run
- 1995
|
|
51
|
Look-ahead value ordering for constraint satisfaction problems
– Frost, Dechter
- 1995
|
|
45
|
A general backtrack algorithm that eliminates most redundant tests
– Gaschnig
- 1977
|
|
44
|
Sparse constraint graphs and exceptionally hard problems
– Smith, Grant
- 1995
|
|
40
|
Domain filtering can degrade intelligent backtracking search
– Prosser
- 1993
|
|
37
|
M.C.: Search Lessons Learned from Crossword Puzzles
– Ginsberg, Frank, et al.
- 1990
|
|
35
|
Tree search and arc-consistency in constraint satisfaction algorithms
– Nadel
- 1988
|
|
34
|
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
– Dechter, Meiri
- 1994
|
|
30
|
MAC-CBJ: maintaining arc consistency with conflict-directed backjumping
– Prosser
- 1995
|
|
28
|
An efficient cross-product representation of the constraint satisfaction problem search space
– Hubbe, Freuder
- 1992
|
|
26
|
Learning while searching in constraint-satisfaction-problems
– Dechter
- 1986
|
|
26
|
In search of the best constraint satisfaction search", to appear
– Frost, Dechter
- 1994
|
|
20
|
Locating the Phase Transition in Constraint Satisfaction Problems
– Smith, Dyer
- 1994
|
|
16
|
Conjunctive width heuristics for maximal constraint satisfaction
– Wallace, Freuder
- 1993
|
|
12
|
Increasing tree seach efficiency for constraint satisfaction problems
– Haralick, Elliot
- 1980
|
|
6
|
Where the exceptionally hard problems are
– Smith, Grant
|
|
2
|
SOLVER
– Puget, Albert
- 1993
|
|
1
|
Systemes a contraintes evolutifs en intelligence artificielle
– Bessiere
- 1992
|
|
1
|
Using local topology to model hard binary constraint satisfaction problems
– Dent, Mercer
- 1995
|
