Applying algorithm selection to abductive diagnostic reasoning

Abstract

The complexity of technical systems requires increasingly advanced fault diagnosis methods to ensure safety and reliability during operation. Particularly in domains where maintenance constitutes an extensive portion of the entire operation cost, efficient and effective failure identification holds the potential to provide large economic value. Abduction offers an intuitive concept for diagnostic reasoning relying on the notion of logical entailment. Nevertheless, abductive reasoning is an intractable problem and computing solutions for instances of reasonable size and complexity persists to pose a challenge. In this paper, we investigate algorithm selection as a mechanism to predict the "best" performing technique for a specific abduction scenario within the framework of model-based diagnosis. Based on a set of structural attributes extracted from the system models, our meta-approach trains a machine learning classifier that forecasts the most runtime efficient abduction technique given a new diagnosis problem. To assess the predictor's selection capabilities and the suitability of the metaapproach in general, we conducted an empirical analysis featuring seven abductive reasoning approaches. The results obtained indicate that applying algorithm selection is competitive in comparison to always choosing a single abductive reasoning method.

Faster horn diagnosis - a performance comparison of abductive reasoning algorithms

“…In previous work [31], we have described several diagnosis algorithms which we implemented in Java as well as exploited available tools realizing the abductive reasoning procedures. The following implementations are used in the empirical evaluation: theories.…”

“…By means of a sample generator, we constructed artificial PHCAPs [31]. The rules contained within the theory are based on several parameters; n is the minimum number of literals in a rule's body.…”

“…Our work is based on previous research. On the one hand, we have assessed abductive inference on models restricted to bijunctive Horn clauses [27,28] and on the other hand, we have developed a meta-approach that has been evaluated in regard to different abduction algorithms [31]. In this paper, we compare the performance of propositional Horn clause abduction within two general directions, i.e., direct and conflict-driven techniques, similar to the work by Nica et al [40] in the consistency-based case.…”