Efficiently Executing Information-Gathering Plans
Abstract
We describe Razor, a planning-based information-gathering agent that assists users by automatically determining which Internet information sites are relevant to their query, accessing those sites in parallel, and integrating the results. Razor uses a disjunctive graph-based plan representation. It then uses a novel and powerful form of local completeness reasoning in order to transform those plans into contingent plans of high quality. These contingent plans can be efficiently executed, obtaining more answers at less cost than the original plans. We focus in this paper on the algorithms underlying the plan transformation process.
Cite
Text
Friedman and Weld. "Efficiently Executing Information-Gathering Plans." International Joint Conference on Artificial Intelligence, 1997.Markdown
[Friedman and Weld. "Efficiently Executing Information-Gathering Plans." International Joint Conference on Artificial Intelligence, 1997.](https://mlanthology.org/ijcai/1997/friedman1997ijcai-efficiently/)BibTeX
@inproceedings{friedman1997ijcai-efficiently,
title = {{Efficiently Executing Information-Gathering Plans}},
author = {Friedman, Marc T. and Weld, Daniel S.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1997},
pages = {785-791},
url = {https://mlanthology.org/ijcai/1997/friedman1997ijcai-efficiently/}
}