Higher-Order Potentialities and their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 262-266.
Abstract
In the development of disciplines addressing dynamics, a major role was played by the assumption that processes can be modelled by introducing state properties, called potentialities, anticipating in which respect a next state will be different. A second assumption often made is that these state properties can be related to other state properties, called reducers. The current paper proposes a philosophical framework in terms of potentialities and their reducers to obtain a common philosophical foundation for methods in AI and Cognitive Science to model dynamics. This framework provides a unified philosophical foundation for numerical, symbolic, and hybrid approaches.
@inproceedings{ijcai2007p40,
title = {Higher-Order Potentialities and their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods},
author = {Bosse, Tibor and Treur, Jan},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {262--266},
year = {2007},
url = {https://ijcai.org/proceedings/2007/40},
}
TY - CPAPER TI - Higher-Order Potentialities and their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods AU - Bosse, Tibor AU - Treur, Jan T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 262 EP - 266 UR - https://ijcai.org/proceedings/2007/40 ER -Copied
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