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Probabilistic continuous constraint satisfaction problems
November 2008
ECB08b
Constraint programming has been used in many applications where uncertainty arises to model safe reasoning. The goal of constraint propagation is to propagate intervals of uncertainty among the variables of the problem, thus only eliminating values that assuredly do not belong to any solution. However, to play safe, these intervals may be very wide and lead to poor propagation. In this paper we present a framework for probabilistic constraint solving that assumes that uncertain values are not all equally likely. Hence, in addition to initial intervals, a priori probability distributions (within these intervals) are defined and propagated through the constraints. This provides a posteriori conditional probabilities for the variables values, thus enabling the user to select the most likely scenarios.
In proceedings
Elsa Carvalho, Jorge Cruz, Pedro Barahona
20th IEEE International Conference on Tools with Artificial Intelligence
-
IEEE
-
2
155-162
978-0-7695-3440-4
1082-3409
-
-
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Elsa Carvalho and Jorge Cruz and Pedro Barahona, Probabilistic continuous constraint satisfaction problems, , 20th IEEE International Conference on Tools with Artificial Intelligence, IEEE, Vol. 2, ISBN 978-0-7695-3440-4, ISSN 1082-3409, Pag. 155-162, November 2008.
<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 continuous constraint satisfaction problems</b>, <u>20th IEEE International Conference on Tools with Artificial Intelligence</u>, <a href="http://www.ieee.org/" title="Link to external entity..." target="_blank" class="publisher">IEEE</a>, Vol. 2, ISBN 978-0-7695-3440-4, ISSN 1082-3409, Pag. 155-162, November 2008.
@inproceedings {ECB08b, author = {Elsa Carvalho and Jorge Cruz and Pedro Barahona}, title = {Probabilistic continuous constraint satisfaction problems}, booktitle = {20th IEEE International Conference on Tools with Artificial Intelligence}, publisher = {IEEE}, volume = {2}, pages = {155-162}, isbn = {978-0-7695-3440-4}, issn = {1082-3409}, abstract = {Constraint programming has been used in many applications where uncertainty arises to model safe reasoning. The goal of constraint propagation is to propagate intervals of uncertainty among the variables of the problem, thus only eliminating values that assuredly do not belong to any solution. However, to play safe, these intervals may be very wide and lead to poor propagation. In this paper we present a framework for probabilistic constraint solving that assumes that uncertain values are not all equally likely. Hence, in addition to initial intervals, a priori probability distributions (within these intervals) are defined and propagated through the constraints. This provides a posteriori conditional probabilities for the variables values, thus enabling the user to select the most likely scenarios.}, month = {November}, year = {2008}, }
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