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Probabilistic reasoning with continuous constraints
September 2007
CaCB07
Continuous constraint reasoning assumes the uncertainty of numerical variables within given bounds and propagates such knowledge through a network of constraints, reducing the uncertainty. In some problems there is also information about the plausibility distribution of values within such bounds. However, the classical constraint framework cannot accommodate that information. This paper describes how the continuous constraint programming paradigm may be extended, in order to accommodate some probabilistic considerations, bridging the gap between the pure interval-based approach, that does not consider likelihoods, and the pure stochastic approach, that does not guarantee the safety of the results obtained.
In proceedings
Elsa Carvalho, Jorge Cruz, Pedro Barahona
T. Simos, G. Psihoyos, Ch. Tsitouras
Proceedings of the Int. Conf. on Numerical Analysis and Applied Mathematics
AIP Conference Proceedings
American Institute of Physics
-
936
105 - 108
978-0-7354-0447-2
0094-243
-
-
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Elsa Carvalho and Jorge Cruz and Pedro Barahona, Probabilistic reasoning with continuous constraints, in: T. Simos and G. Psihoyos and Ch. Tsitouras (eds), Proceedings of the Int. Conf. on Numerical Analysis and Applied Mathematics, AIP Conference Proceedings, American Institute of Physics, Vol. 936, ISBN 978-0-7354-0447-2, ISSN 0094-243, Pag. 105 - 108, September 2007.
<a href="/people/members/view.php?code=8d1b2918d558af8e9308270b485b62a8" class="author">Elsa Carvalho</a>, <a href="/people/members/view.php?code=3f6f0c9973cdaeab1a3dd815682bb0ac" class="author">Jorge Cruz</a> and <a href="/people/members/view.php?code=7e27bc13fad97e99cd21ea6914d55659" class="author">Pedro Barahona</a>, <b>Probabilistic reasoning with continuous constraints</b>, in: T. Simos, G. Psihoyos and Ch. Tsitouras (eds), <u>Proceedings of the Int. Conf. on Numerical Analysis and Applied Mathematics</u>, AIP Conference Proceedings, <a href="http://www.aip.org/" title="Link to external entity..." target="_blank" class="publisher">American Institute of Physics</a>, Vol. 936, ISBN 978-0-7354-0447-2, ISSN 0094-243, Pag. 105 - 108, September 2007.
@inproceedings {CaCB07, author = {Elsa Carvalho and Jorge Cruz and Pedro Barahona}, editor = {T. Simos and G. Psihoyos and Ch. Tsitouras}, title = {Probabilistic reasoning with continuous constraints}, booktitle = {Proceedings of the Int. Conf. on Numerical Analysis and Applied Mathematics}, series = {AIP Conference Proceedings}, publisher = {American Institute of Physics}, volume = {936}, pages = {105 - 108}, isbn = {978-0-7354-0447-2}, issn = {0094-243}, abstract = {Continuous constraint reasoning assumes the uncertainty of numerical variables within given bounds and propagates such knowledge through a network of constraints, reducing the uncertainty. In some problems there is also information about the plausibility distribution of values within such bounds. However, the classical constraint framework cannot accommodate that information. This paper describes how the continuous constraint programming paradigm may be extended, in order to accommodate some probabilistic considerations, bridging the gap between the pure interval-based approach, that does not consider likelihoods, and the pure stochastic approach, that does not guarantee the safety of the results obtained.}, month = {September}, year = {2007}, }
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