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The Goldilocks problem

Published online by Cambridge University Press:  07 August 2003

TUDOR HULUBEI ,
EUGENE C. FREUDER  and
RICHARD J. WALLACE
TUDOR HULUBEI
Affiliation:
Department of Computer Science, University of New Hampshire, Durham, New Hampshire, USA
EUGENE C. FREUDER
Affiliation:
Cork Constraint Computation Centre, Computer Science Department, University College Cork, Cork, Ireland
RICHARD J. WALLACE
Affiliation:
Cork Constraint Computation Centre, Computer Science Department, University College Cork, Cork, Ireland
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Abstract

Constraint-based reasoning is often used to represent and find solutions to configuration problems. In the field of constraint satisfaction, the major focus has been on finding solutions to difficult problems. However, many real-life configuration problems, although not extremely complicated, have a huge number of solutions, few of which are acceptable from a practical standpoint. In this paper we present a value ordering heuristic for constraint solving that attempts to guide search toward solutions that are acceptable. More specifically, by considering weights that are assigned to values and sets of values, the heuristic can guide search toward solutions for which the total weight is within an acceptable interval. Experiments with random constraint satisfaction problems demonstrate that, when a problem has numerous solutions, the heuristic makes search extremely efficient even when there are relatively few solutions that fall within the interval of acceptable weights. In these cases, an algorithm that is very effective for finding a feasible solution to a given constraint satisfaction problem (the “maintained arc consistency” algorithm or MAC) does not find a solution in the same weight interval within a reasonable time when it is run without the heuristic.

Type
Research Article
Information
AI EDAM , Volume 17 , Issue 1 , February 2003 , pp. 3 - 11
Copyright
© 2003 Cambridge University Press

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