Ravestate: Distributed Composition of a Causal-Specificity-Guided Interaction Policy

• URL: http://arxiv.org/abs/2310.01943v1
• Date: Tue, 3 Oct 2023 10:38:53 GMT
• Title: Ravestate: Distributed Composition of a Causal-Specificity-Guided Interaction Policy
• Authors: Joseph Birkner, Andreas Dolp, Negin Karimi, Nikita Basargin, Alona Kharchenko and Rafael Hostettler
• Abstract summary: In human-robot interaction policy design, a rule-based method is efficient, explainable, expressive and intuitive. We present the Signal-Rule-Slot framework, which refines prior work on rule-based symbol system design. We introduce a new, Bayesian notion of interaction rule utility called Causal Pathway Self-information.
• Score: 0.8039067099377079
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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