On the Generation of Alternative Explanations with Implications for Belief Revision
Abstract
In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of message-passing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly efficient manner. This approach is then applied to cost-based abduction problems as well as belief revision in Bayesian net works.
Cite
Text
Santos. "On the Generation of Alternative Explanations with Implications for Belief Revision." Conference on Uncertainty in Artificial Intelligence, 1991.Markdown
[Santos. "On the Generation of Alternative Explanations with Implications for Belief Revision." Conference on Uncertainty in Artificial Intelligence, 1991.](https://mlanthology.org/uai/1991/santos1991uai-generation/)BibTeX
@inproceedings{santos1991uai-generation,
title = {{On the Generation of Alternative Explanations with Implications for Belief Revision}},
author = {Santos, Eugene},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1991},
url = {https://mlanthology.org/uai/1991/santos1991uai-generation/}
}