Intelligent Resilience Testing for Decision-Making Agents with Dual-Mode Surrogate Adaptation

TL;DR

This paper introduces IRTest, an adaptive online framework that enhances the testing of decision-making agents by reducing surrogate model gaps through neural fine-tuning and importance sampling, improving robustness and generalizability.

Contribution

The paper presents IRTest, a novel online adaptive testing framework combining neural fine-tuning and importance sampling to improve surrogate-based testing of decision agents.

Findings

IRTest improves failure discovery efficiency.

IRTest enhances testing robustness across systems.

IRTest demonstrates strong generalizability in experiments.

Abstract

Testing and evaluating decision-making agents remains challenging due to unknown system architectures, limited access to internal states, and the vastness of high-dimensional scenario spaces. Existing testing approaches often rely on surrogate models of decision-making agents to generate large-scale scenario libraries; however, discrepancies between surrogate models and real decision-making agents significantly limit their generalizability and practical applicability. To address this challenge, this paper proposes intelligent resilience testing (IRTest), a unified online adaptive testing framework designed to rapidly adjust to diverse decision-making agents. IRTest initializes with an offline-trained surrogate prediction model and progressively reduces surrogate-to-real gap during testing through two complementary adaptation mechanisms: (i) online neural fine-tuning in data-rich regimes, and (ii) lightweight importance-sampling-based weighting correction in data-limited regimes. A Bayesian optimization strategy, equipped with bias-corrected acquisition functions, guides scenario generation to balance exploration and exploitation in complex testing spaces. Extensive experiments across varying levels of task complexity and system heterogeneity demonstrate that IRTest consistently improves failure-discovery efficiency, testing robustness, and cross-system generalizability. These results highlight the potential of IRTest as a practical solution for scalable, adaptive, and resilient testing of decision-making agents.