Faster horn diagnosis - a performance comparison of abductive reasoning algorithms

Roxane Koitz-Hristov*, Franz Wotawa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Abductive inference derives explanations for encountered anomalies and thus embodies a natural approach for diagnostic reasoning. Yet its computational complexity, which is inherent to the expressiveness of the underlying theory, remains a disadvantage. Even when restricting the representation to Horn formulae the problem is NP-complete. Hence, finding procedures that can efficiently solve abductive diagnosis problems is of particular interest from a research as well as practical point of view. In this paper, we aim at providing guidance on choosing an algorithm or tool when confronted with the issue of computing explanations in propositional logic-based abduction. Our focus lies on Horn representations, which provide a suitable language to describe most diagnostic scenarios. We illustrate abduction via two contrasting problem formulations: direct proof methods and conflict-driven techniques. While the former is based on determining logical consequences, the later searches for suitable refutations involving possible causes. To reveal runtime performance trends we conducted a case study, in which we compared publicly available general purpose tools, established Horn reasoning engines, as well as new variations of known methods as a means for abduction.
Original languageEnglish
Pages (from-to)1558-1572
Number of pages15
JournalApplied Intelligence
Issue number5
Early online dateJan 2020
Publication statusPublished - 1 May 2020


  • Abductive diagnosis
  • Abductive reasoning
  • Model-based diagnosis

ASJC Scopus subject areas

  • Artificial Intelligence

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