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

TL;DR

This paper introduces Ravestate, a probabilistic rule-based framework for human-robot interaction that enhances contextual understanding and interaction policy design through a Bayesian approach, supported by theoretical foundations and user studies.

Contribution

It presents the Signal-Rule-Slot framework with a novel Bayesian measure called Causal Pathway Self-information, advancing rule-based interaction systems.

Findings

Robust contextual behavior demonstrated in user studies

Effective human-machine interaction achieved

Framework supports text, speech, and vision scenarios

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.