Improving Abductive Diagnosis Through Structural Features: A Meta-Approach

While abductive reasoning provides an intuitive approach
to diagnosis, its computational complexity remains an obstacle.
Even though certain model representations are tractable, computing
solutions for instances of reasonable size and complexity persists
to pose a challenge. Hence, the discovery of efficient methods
to derive abductive explanations presents itself as appealing research
area. In this paper, we investigate the structural properties inherent
to formalizations suitable for abductive failure localization. Based
on the features extracted we construct a meta-approach exploiting a
machine learning classifier to predict the abductive reasoning technique
yielding the “best” performance on a specific diagnosis scenario.
To assess whether the proposed attributes are in fact sufficient
for forecasting the appropriate abduction procedure and to evaluate
the efficiency of our algorithm selection in comparison to traditional
abductive reasoning approaches, we conducted an empirical experiment.
The results obtained indicate that the trained model is capable
of predicting the most efficient algorithm and further, we can show
that the meta-approach is capable of outperforming each single abductive
reasoning method investigated.