An Integrated Framework for Learning and Reasoning
Abstract
Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.
Cite
Text
Giraud-Carrier and Martinez. "An Integrated Framework for Learning and Reasoning." Journal of Artificial Intelligence Research, 1995. doi:10.1613/JAIR.93Markdown
[Giraud-Carrier and Martinez. "An Integrated Framework for Learning and Reasoning." Journal of Artificial Intelligence Research, 1995.](https://mlanthology.org/jair/1995/giraudcarrier1995jair-integrated/) doi:10.1613/JAIR.93BibTeX
@article{giraudcarrier1995jair-integrated,
title = {{An Integrated Framework for Learning and Reasoning}},
author = {Giraud-Carrier, Christophe G. and Martinez, Tony R.},
journal = {Journal of Artificial Intelligence Research},
year = {1995},
pages = {147-185},
doi = {10.1613/JAIR.93},
volume = {3},
url = {https://mlanthology.org/jair/1995/giraudcarrier1995jair-integrated/}
}