Ravestate: Distributed Composition of a Causal-Specificity-Guided Interaction Policy
Robotics
2023-10-04 v1 Artificial Intelligence
Human-Computer Interaction
Abstract
In human-robot interaction policy design, a rule-based method is efficient, explainable, expressive and intuitive. In this paper, we present the Signal-Rule-Slot framework, which refines prior work on rule-based symbol system design and introduces a new, Bayesian notion of interaction rule utility called Causal Pathway Self-information. We offer a rigorous theoretical foundation as well as a rich open-source reference implementation Ravestate, with which we conduct user studies in text-, speech-, and vision-based scenarios. The experiments show robust contextual behaviour of our probabilistically informed rule-based system, paving the way for more effective human-machine interaction.
Cite
@article{arxiv.2310.01943,
title = {Ravestate: Distributed Composition of a Causal-Specificity-Guided Interaction Policy},
author = {Joseph Birkner and Andreas Dolp and Negin Karimi and Nikita Basargin and Alona Kharchenko and Rafael Hostettler},
journal= {arXiv preprint arXiv:2310.01943},
year = {2023}
}