Uncertainty in Artificial Intelligence
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A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning
Ronald Yager
Abstract:
We describe a technique that can be used for the fusion of multiple sources of information as well as for the evaluation and selection of alternatives under multi-criteria. Three important properties contribute to the uniqueness of the technique introduced. The first is the ability to do all necessary operations and aggregations with information that is of a nonnumeric linguistic nature. This facility greatly reduces the burden on the providers of information, the experts. A second characterizing feature is the ability assign, again linguistically, differing importance to the criteria or in the case of information fusion to the individual sources of information. A third significant feature of the approach is its ability to be used as method to find a consensus of the opinion of multiple experts on the issue of concern. The techniques used in this approach are base on ideas developed from the theory of approximate reasoning. We illustrate the approach with a problem of project selection.
Keywords:
Pages: 433-437
PS Link:
PDF Link: /papers/91/p433-yager.pdf
BibTex:
@INPROCEEDINGS{Yager91,
AUTHOR = "Ronald Yager ",
TITLE = "A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based on Approximate Reasoning",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "433--437"
}


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