{'fraction_non_alphanumeric': 0.0239880059970015, 'fraction_numerical': 0.005997001499250375, 'mean_word_length': 5.958333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hierarchical Diagnosis Guided by Observations

We propose a technique to improve the performance of hierarchical model-based diagnosis, based on structural abstraction. Given a hierarchical representation and the set of currently available observations, the technique is able to dynamically derive a tailored hierarchical representation to diagnose the current situation. We implement our strategy as an extension to the well-known Mozetic's approach [Mozetic, 1992], and illustrate the obtained performance improvements. Our approach is more efficient than Mozetic's one when, due to abstraction, fewer observations are available at the coarsest hierarchical levels.