Computational Methods for a Mathematical Theory of Evidence
Proceedings of the Seventh International Joint Conference on Artificial Intelligence. Expert Systems. Pages 868-874.
Abstract
Many knowledge-based expert systems employ numerical schemes to represent evidence. rate competing hypoth · eses, and guide search through the domain's problem space. This paper has two objectives: first. to introduce one such scheme . developed by Arthur Dempster and Glen Shafer, to a wider audience: second, to present results that can reduce the compu · tation -time complexity from exponential to linear. allowing this scheme to be implemented in many more systems. In order to enjoy this reduction, some assumptions about the structure of the type of evidence represented and combined must be made . The assumption made here is that each piece of the evidence either confirms or denies a single proposition rather than a disjunction . For any domain in which the assumption is justified . the savings are available.
@inproceedings{ijcai1981p166,
title = {Computational Methods for a Mathematical Theory of Evidence},
author = {Barnett, Jeffrey A.},
booktitle = {Proceedings of the Seventh International Joint Conference on
Artificial Intelligence, {IJCAI-81}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Roger C. Schank},
pages = {868--874},
year = {1981},
month = {8},
note = {Expert Systems},
url = {https://ijcai.org/proceedings/1981/166},
}
TY - CPAPER TI - Computational Methods for a Mathematical Theory of Evidence AU - Barnett, Jeffrey A. T2 - Proceedings of the Seventh International Joint Conference on Artificial Intelligence, IJCAI-81 PB - International Joint Conferences on Artificial Intelligence PY - 1981 SP - 868 EP - 874 UR - https://ijcai.org/proceedings/1981/166 ER -Copied
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