GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework for Autonomous Systems

TL;DR

GRAVITAS leverages model checking techniques to enhance planning and goal reasoning in autonomous systems, providing trustworthy plans in uncertain environments by systematically modeling goal task networks within a formal verification framework.

Contribution

This paper introduces a novel framework called GRAVITAS that applies model checking to goal reasoning and planning, specifically reformulating Goal Task Networks for formal verification.

Findings

Successfully modeled GTNs in the Process Analysis Toolkit (PAT)

Demonstrated trustworthy planning in an autonomous underwater vehicle simulation

Enhanced interpretability and reliability of autonomous system behaviors

Abstract

While AI techniques have found many successful applications in autonomous systems, many of them permit behaviours that are difficult to interpret and may lead to uncertain results. We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems. We give a new formulation of Goal Task Network (GTN) that is tailored for our model checking based framework. We then provide a systematic method that models GTNs in the model checker Process Analysis Toolkit (PAT). We present our planning and goal reasoning system as a framework called Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in an uncertain environment. Finally, we demonstrate the proposed ideas in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.