Probabilistic Conditional System Invariant Generation with Bayesian Inference

TL;DR

This paper introduces a Bayesian inference-based method to automatically generate probabilistic invariants for complex, stateful autonomous systems, uncovering hidden behavioral properties from trace data.

Contribution

It presents a novel approach for inferring conditional probabilistic invariants using Bayesian inference, tailored for rich, stochastic systems like autonomous robots.

Findings

Successfully identified hidden stateful invariants in robotic systems

Enhanced understanding of system behavior through probabilistic invariants

Demonstrated effectiveness on two semi-autonomous mobile robots

Abstract

Invariants are a set of properties over program attributes that are expected to be true during the execution of a program. Since developing those invariants manually can be costly and challenging, there are a myriad of approaches that support automated mining of likely invariants from sources such as program traces. Existing approaches, however, are not equipped to capture the rich states that condition the behavior of autonomous mobile robots, or to manage the uncertainty associated with many variables in these systems. This means that valuable invariants that appear only under specific states remain uncovered. In this work we introduce an approach to infer conditional probabilistic invariants to assist in the characterization of the behavior of such rich stateful, stochastic systems. These probabilistic invariants can encode a family of conditional patterns, are generated using Bayesian inference to leverage observed trace data against priors gleaned from previous experience and expert knowledge, and are ranked based on their surprise value and information content. Our studies on two semi-autonomous mobile robotic systems show how the proposed approach is able to generate valuable and previously hidden stateful invariants.