TY - CHAP
T1 - Uncertainty and change
AU - Brown, Kenneth N.
AU - Miguel, Ian
N1 - Funding: Ken Brown's work was supported in part by grants 03/CE3/I405 (SFI Centre for Telecommunications Value-chain Research) and SC/2003/81 (Enterprise Ireland). Ian Miguel is supported by a UK Royal Academy of Engineering/EPRSC Research Fellowship.
PY - 2006
Y1 - 2006
N2 - Many real and important problems involve change and uncertainty. Solutions are required that take account of vagueness in the problem description, or that minimise the effect of the uncertainty on the solution. Basic approaches to handling change include rapid reaction through re-specifying the problems and re-solving when the changes occur, preparing to change by maintaining explanations and data structures that will allow the solver to avoid repeating work, or proactively generating solutions that are robust, by explicitly reasoning about the possible changes. A number of different techniques have been developed, and they have demonstrated that constraint programming methods can be extended to handle many different forms of dynamism and uncertainty, and that many exemplar problems can be solved efficiently. Constraint programming toolkits need to be extended with facilities to handle such problems. Further work is required to establish which of the techniques and frameworks are practical candidates, and to integrate this body of research with the many other research fields which deal with change and uncertainty. Finally, for an alternative viewpoint on the material in this chapter, the reader is directed to the survey by Verfaillie and Jussien [72].
AB - Many real and important problems involve change and uncertainty. Solutions are required that take account of vagueness in the problem description, or that minimise the effect of the uncertainty on the solution. Basic approaches to handling change include rapid reaction through re-specifying the problems and re-solving when the changes occur, preparing to change by maintaining explanations and data structures that will allow the solver to avoid repeating work, or proactively generating solutions that are robust, by explicitly reasoning about the possible changes. A number of different techniques have been developed, and they have demonstrated that constraint programming methods can be extended to handle many different forms of dynamism and uncertainty, and that many exemplar problems can be solved efficiently. Constraint programming toolkits need to be extended with facilities to handle such problems. Further work is required to establish which of the techniques and frameworks are practical candidates, and to integrate this body of research with the many other research fields which deal with change and uncertainty. Finally, for an alternative viewpoint on the material in this chapter, the reader is directed to the survey by Verfaillie and Jussien [72].
UR - https://doi.org/10.1016/s1574-6526(06)x8001-x
UR - https://discover.libraryhub.jisc.ac.uk/search?q=isn%3A%209780444527264&rn=1
U2 - 10.1016/S1574-6526(06)80025-8
DO - 10.1016/S1574-6526(06)80025-8
M3 - Chapter
AN - SCOPUS:77956786694
SN - 9780444527264
T3 - Foundations of artificial intelligence (Elsevier)
SP - 731
EP - 760
BT - Handbook of constraint programming
A2 - Rossi, Francesca
A2 - van Beek, Peter
A2 - Walsh, Toby
PB - Elsevier
CY - Amsterdam
ER -