PUMA
Istituto di Informatica e Telematica     
Bistarelli S. Interchangeability with thresholds and degradation factors for Soft CSPs. In: Annals of Mathematics and Artificial Intelligence, vol. 67 (2) pp. 123 - 163. Springer, 2013.
 
 
Abstract
(English)
Substitutability and interchangeability in constraint satisfaction problems (CSPs) have been used as a basis for search heuristics, solution adaptation and abstraction techniques. In this paper, we consider how the same concepts can be extended to soft constraint satisfaction problems (SCSPs). We introduce two notions: threshold alpha and degradation factor delta for substitutability and interchangeability, ( (alpha) substitutability/interchangeability and (delta) substitutability/interchangeabi-lity respectively). We show that they satisfy analogous theorems to the ones already known for hard constraints. In (alpha) interchangeability, values are interchangeable in any solution that is better than a threshold alpha, thus allowing to disregard differences among solutions that are not sufficiently good anyway. In (delta) interchangeability, values are interchangeable if their exchange could not degrade the solution by more than a factor of delta. We give efficient algorithms to compute ( (delta) / (alpha) )interchangeable sets of values for a large class of SCSPs, and show an example of their application. Through experimental evaluation based on random generated problem we measure first, how often neighborhood interchangeable values are occurring, second, how well they can approximate fully interchangeable ones, and third, how efficient they are when used as preprocessing techniques for branch and bound search.
Subject Interchangeability
Constraint optimization
Soft constraints
Constraint satisfaction
I.2.4 Knowledge Representation Formalisms and Methods


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional