Generalized Physical Networks for Automated Model Building
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Qualitative Reasoning and Diagnosis. Pages 1047-1052.
Abstract
We pr ese nt a new knowledge representation and reasoning framework for mod eling nonlinear dynamical systems. The goa ls of this framework are to smoothly incorporate varying levels of domain knowledge and to tailor the reasoning methods - and hence th e search space - accordingly. Our solution exploits generalized physical networks (GPN), a meta-level representation of idealized two-terminal elements, tog et her with a hierarchy of qualitative and quantitative analysis tools, to produce a dynamic modeling domain whose complexity naturally adapts to the amount of available information about the target system.
@inproceedings{ijcai1999p149,
title = {Generalized Physical Networks for Automated Model Building},
author = {Easley, Matthew and Bradley, Elizabeth},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {1047--1052},
year = {1999},
note = {Qualitative Reasoning and Diagnosis},
url = {https://ijcai.org/proceedings/1999/149},
}
TY - CPAPER TI - Generalized Physical Networks for Automated Model Building AU - Easley, Matthew AU - Bradley, Elizabeth T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 1047 EP - 1052 UR - https://ijcai.org/proceedings/1999/149 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
