Inferring and Conveying
Intentionality: Beyond Numerical Rewards to Logical Intentions
Susmit Jha and John Rushby
Presented at TOCAIS 2019:
Towards Conscious AI Systems Symposium (part of AAAI SSS-19), Stanford
CA, March 25-27, 2019
Abstract
Shared intentionality is a critical component in developing conscious
AI agents capable of collaboration, self-reflection, deliberation, and
reasoning. We formulate inference of shared intentionality as an
inverse reinforcement learning problem with logical reward
specifications. We show how the approach can infer task descriptions
from demonstrations. We also extend our approach to actively convey
intentionality. We demonstrate the approach on a simple grid-world
example.
Proceedings, and
our paper (pdf), also available as
arXiv 2207.05058
BibTeX Entry
@inproceedings{Jha&Rushby19,
AUTHOR = {Susmit Jha and John Rushby},
TITLE = {Inferring and Conveying Intentionality: Beyond Numerical
Rewards to Logical Intentions},
BOOKTITLE = {Towards Conscious AI Systems Symposium (TOCAIS):
AAAI Spring Symposium Series},
YEAR = 2019,
EDITOR = {Antonio Chella and others},
ADDRESS = {Stanford, CA},
MONTH = mar
}
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