Higher-Order Potentialities and Their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods
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.
Cite
Text
Bosse and Treur. "Higher-Order Potentialities and Their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Bosse and Treur. "Higher-Order Potentialities and Their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/bosse2007ijcai-higher/)BibTeX
@inproceedings{bosse2007ijcai-higher,
title = {{Higher-Order Potentialities and Their Reducers: A Philosophical Foundation Unifying Dynamic Modeling Methods}},
author = {Bosse, Tibor and Treur, Jan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {262-267},
url = {https://mlanthology.org/ijcai/2007/bosse2007ijcai-higher/}
}