Preference Moore Machines for Neural Fuzzy Integration
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 840-844.
Abstract
This paper describes multidimensional neural preference classes and preference Moore machines as a principle for integrating different neural and/or symbolic knowledge sources. We relate neural preferences to multidimensional fuzzy set representations. Furthermore, we introduce neural preference Moore machines and relate traditional symbolic transducers with simple recurrent networks by using neural preference Moore machines. Finally, we demonstrate how the concepts of preference classes and preference Moore machin es can be used to integrate knowledge from different neural and/or symbolic machines. We argue that our new concepts for preference Moore machines contribute a new potential approach towards general principles of neural symbolic integration.
@inproceedings{ijcai1999p120,
title = {Preference Moore Machines for Neural Fuzzy Integration},
author = {Wermter, Stefan},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {840--844},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/120},
}
TY - CPAPER TI - Preference Moore Machines for Neural Fuzzy Integration AU - Wermter, Stefan T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 840 EP - 844 UR - https://ijcai.org/proceedings/1999/120 ER -Copied
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