Cognition Envelopes for Bounded Decision Making in Autonomous UAS Operations

TL;DR

This paper introduces Cognition Envelopes to establish reasoning boundaries for AI in autonomous UAS, enhancing decision reliability amidst model errors, with a focus on SAR missions.

Contribution

It proposes a novel framework of Cognition Envelopes combining probabilistic reasoning and resource analysis for AI decision safety in cyber-physical systems.

Findings

Effective decision constraints in SAR missions

Identification of software engineering challenges

Validation through real mission assessments

Abstract

Cyber-physical systems increasingly rely on foundational models, such as Large Language Models (LLMs) and Vision-Language Models (VLMs) to increase autonomy through enhanced perception, inference, and planning. However, these models also introduce new types of errors, such as hallucinations, over-generalizations, and context misalignments, resulting in incorrect and flawed decisions. To address this, we introduce the concept of Cognition Envelopes, designed to establish reasoning boundaries that constrain AI-generated decisions while complementing the use of meta-cognition and traditional safety envelopes. As with safety envelopes, Cognition Envelopes require practical guidelines and systematic processes for their definition, validation, and assurance. In this paper we describe an LLM/VLM-supported pipeline for dynamic clue analysis within the domain of small autonomous Uncrewed Aerial Systems deployed on Search and Rescue (SAR) missions, and a Cognition Envelope based on probabilistic reasoning and resource analysis. We evaluate the approach through assessing decisions made by our Clue Analysis Pipeline in a series of SAR missions. Finally, we identify key software engineering challenges for systematically designing, implementing, and validating Cognition Envelopes for AI-supported decisions in cyber-physical systems.