OSB > Neil Yorke-Smith > Research > Publications
Data Uncertainty in Constraint Programming: A Non-Probabilistic ApproachYorke-Smith, N. and Gervet, C., Data Uncertainty in Constraint Programming: A Non-Probabilistic Approach. Proceedings of the AAAI 2001 Fall Symposium on Using Uncertainty within Computation, Cape Cod, MA, November 2001. Abstract: The constraint programming paradigm has proved to have
the flexibility and efficiency necessary to treat well-defined large-scale
optimisation (LSCO) problems. Many real world problems, however, are
ill-defined, incomplete, or have uncertain data. Research on ill-defined
LSCO problems has centred on modelling the uncertainties by approximating
the state of the real world, with no guarantee as a result that the actual
problem is being solved. We focus here on ill-defined data, motivated by
problems from energy trading and computer network optimisation, where no
probability distribution is known or can be usefully obtained. We suggest
a non-probabilistic
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