Self-adaptation via joint global-local explanations for autonomous cyber-physical systems

Context:
Autonomous and self-adaptive systems increasingly rely on black-box predictive models to support runtime decision making in critical domains. However, the lack of transparency of such models enlarges the adaptation search space and increases optimization costs.
Objective:
This paper addresses the problem of multi-objective optimization in opaque autonomous systems through cost-effective explanation-driven adaptation.
Method:
We propose GLASS, a self-adaptation approach that integrates model-agnostic interpretable machine learning into a feedback loop. Building on our previous work, GLASS combines global explanations (Partial Dependence Plots) with local explanations (counterfactuals) to guide runtime adaptation decisions and reduce the search space toward goal-relevant configurations.
Results:
An empirical evaluation on three cyber–physical systems benchmarks shows that GLASS achieves competitive or superior adaptation effectiveness while significantly reducing execution time compared to three state-of-the-art baselines, including up to two orders of magnitude lower overhead than black-box optimization.
Conclusion:
... mehrGLASS enables transparent and efficient runtime self-adaptation by jointly leveraging global and local explanations to optimize adaptation search while preserving effectiveness.