Faster horn diagnosis - a performance comparison of abductive reasoning algorithms

Roxane Koitz-Hristov*, Franz Wotawa

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

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.
Originalspracheenglisch
Seiten (von - bis)1558-1572
Seitenumfang15
FachzeitschriftApplied Intelligence
Jahrgang50
Ausgabenummer5
Frühes Online-DatumJan 2020
DOIs
PublikationsstatusVeröffentlicht - 1 Mai 2020

ASJC Scopus subject areas

  • Artificial intelligence

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