BeliefNet: a neurosymbolic model for context-based traversability predictions in complex environments

Abstract

Knowing how to traverse complex unstructured environments is a difficult challenge, that humans achieve through logic, reasoning, and experience; yet some of the most beneficial use cases for autonomous systems require them to operate in complex environments without regular human intervention. Furthermore, for machines to support humans in such use cases, trust in decision making will be crucial, ensuring operators have confidence to deploy the capabilities. Despite its importance, enabling autonomous agents to navigate effectively and reliably in complex terrain remains an unsolved challenge. Advances in neurosymbolic artificial intelligence present an opportunity to enhance performance in complex, explainable, and uncertain decision making, such as autonomous traversability analysis. The challenge of complex environments is complicated by its non-deterministic nature; terrain will adapt and change through domains, and its properties can adapt rapidly based on external factors like weather or objects that are in proximity, which is true for one location on one day, will not persist. This article presents a new neurosymbolic model structure that was designed specifically for this task. It uses experience to build a world model, similar to that of a neural network, but with some key delineating features such as full explainability, through life adaptation or evolution, and zero-shot capability. This provides the reasoning backbone for an autonomous agent to determine the level of risk each object presents based on its context and therefore determine the best possible route.