Case-based Modeling with Qualitative Indices
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Qualitative Reasoning and Diagnosis. Pages 1757-1764.
Abstract
One of the challenges in process control is providing reliable control of poorly understood systems. Before such a system can be controlled we must first be able to predict its future behavior-so that we know what control action is necessary. This paper presents two approaches to this prediction task, both using qualitative models augmented by records of historical system behavior. Our hypothesis is that qualitative information about a system is more easily available than quantitative equations; moreover, the information need not be complete or totally correct. We restructure the historical information into a case-base suitable for the prediction task, and use the qualitative model to identify the attributes to use as case-indices. The case-base then provides the quantitative information needed for the prediction task. Our techniques are extensively evaluated on data taken from a real-world system.
@inproceedings{ijcai1995p226,
title = {Case-based Modeling with Qualitative Indices},
author = {Richards, Bradley L. and Faltings, Boi and Duxbury-Smith, Peter},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1757--1764},
year = {1995},
note = {Qualitative Reasoning and Diagnosis},
url = {https://ijcai.org/proceedings/1995/226},
}
TY - CPAPER TI - Case-based Modeling with Qualitative Indices AU - Richards, Bradley L. AU - Faltings, Boi AU - Duxbury-Smith, Peter T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1757 EP - 1764 UR - https://ijcai.org/proceedings/1995/226 ER -Copied
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