Vocabulary Alignment in Openly Specified Interactions

Abstract

The problem of achieving common understanding between agents that use different vocabularies has been mainly addressed by techniques that assume the existence of shared external elements, such as a meta-language or a physical environment. In this article, we consider agents that use different vocabularies and only share knowledge of how to perform a task, given by the specification of an interaction protocol. We present a framework that lets agents learn a vocabulary alignment from the experience of interacting. Unlike previous work in this direction, we use open protocols that constrain possible actions instead of defining procedures, making our approach more general. We present two techniques that can be used either to learn an alignment from scratch or to repair an existent one, and we evaluate their performance experimentally.

Cite

Text

Chocron. "Vocabulary Alignment in Openly Specified Interactions." Journal of Artificial Intelligence Research, 2020. doi:10.1613/JAIR.1.11497

Markdown

[Chocron. "Vocabulary Alignment in Openly Specified Interactions." Journal of Artificial Intelligence Research, 2020.](https://mlanthology.org/jair/2020/chocron2020jair-vocabulary/) doi:10.1613/JAIR.1.11497

BibTeX

@article{chocron2020jair-vocabulary,
  title     = {{Vocabulary Alignment in Openly Specified Interactions}},
  author    = {Chocron, Paula Daniela},
  journal   = {Journal of Artificial Intelligence Research},
  year      = {2020},
  pages     = {69-107},
  doi       = {10.1613/JAIR.1.11497},
  volume    = {68},
  url       = {https://mlanthology.org/jair/2020/chocron2020jair-vocabulary/}
}