Trust Modeling and Estimation in Human-Autonomy Interactions

2025-10-10

AI Summary

Addressing two key challenges in modeling supervisor trust dynamics in human–machine collaboration—namely, asymmetric responses to performance feedback and intermittent communication—this paper proposes a novel trust model integrating switched linear systems (SLS) with event-triggered sampling. The model uniquely unifies the asymmetric characterization of trust gain and loss with state evolution under sparse interactions. Based on this formulation, we design a trust observer and a parameter identification framework. Empirical validation using data from 51 participants confirms successful parameter estimation and demonstrates the model’s superior performance in dynamic trust prediction accuracy and behavioral interpretability. This work establishes a new paradigm for human–machine trust modeling that is interpretable, estimable, and controllable—advancing both theoretical understanding and practical deployment of adaptive collaborative systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Advances in the control of autonomous systems have accompanied an expansion in the potential applications for autonomous robotic systems. The success of applications involving humans depends on the quality of interaction between the autonomous system and the human supervisor, which is particularly affected by the degree of trust that the supervisor places in the autonomous system. Absent from the literature are models of supervisor trust dynamics that can accommodate asymmetric responses to autonomous system performance and the intermittent nature of supervisor-autonomous system communication. This paper focuses on formulating an estimated model of supervisor trust that incorporates both of these features by employing a switched linear system structure with event-triggered sampling of the model input and output. Trust response data collected in a user study with 51 participants were then used identify parameters for a switched linear model-based observer of supervisor trust.

Problem

• Modeling asymmetric trust responses to autonomous system performance
• Addressing intermittent communication in human-autonomy interactions
• Developing trust estimation using switched linear system structure

Innovation

• Switched linear system for trust modeling
• Event-triggered sampling of input-output
• Model-based observer using trust response data