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A neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge is proposed.
This study systematically investigates how prompt structure, model architecture, and environment representation affect the quality of Explainable RL (XRL) explanations, and conducts a multi-model evaluation using quantitative metrics to assess explanation coherence, helpfulness, and stability.
The pipeline extracts a discrete-time Markov chain from the SUT's token-by-token generation, verifies formal PCTL specifications with the PRISM model checker, and aggregates the per-input verdicts into a coverage curve over the input space, identifying the gap between structural completeness and chemical validity.
Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, demonstrating the method's effectiveness in data-scarce manufacturing environments.
The results indicate how trustworthy LLM-generated explanations are in model-free settings, where the same LLMs are used but no oracle exists to verify them.
Gimitest is an open-source tool that supports various gym frameworks and allows for modifications of their integrated components and showcases its effectiveness in testing multiple RL policies in environments such as the official Farama Gymnasium and PettingZoo.
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