Preprint
Learning Norms via Natural Language Teachings
ArXiv.org
Cornell University
01/20/2022
DOI: 10.48550/arxiv.2201.10556
Abstract
To interact with humans, artificial intelligence (AI) systems must understand
our social world. Within this world norms play an important role in motivating
and guiding agents. However, very few computational theories for learning
social norms have been proposed. There also exists a long history of debate on
the distinction between what is normal (is) and what is normative (ought). Many
have argued that being capable of learning both concepts and recognizing the
difference is necessary for all social agents. This paper introduces and
demonstrates a computational approach to learning norms from natural language
text that accounts for both what is normal and what is normative. It provides a
foundation for everyday people to train AI systems about social norms.
Details
- Title: Subtitle
- Learning Norms via Natural Language Teachings
- Creators
- Taylor OlsonKen Forbus
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2201.10556
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 01/20/2022
- Academic Unit
- Computer Science
- Record Identifier
- 9984958643102771
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