Memory-limited model-based diagnosis

• Published in: Artificial intelligence Vol. 305; p. 103681
• Main Author: Rodler, Patrick
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.04.2022; Elsevier Science Ltd
• Subjects: Algorithms; Combinatorial search; Completeness; Computation; Diagnosis; Diagnostic systems; Domains; Fault isolation; Fault localization; Heuristic search; Hitting set computation; HS-tree; Interactive debugging; Knowledge base debugging; Linear best-first hitting set search; Linear best-first search; Memory devices; Memory-limited diagnosis search; Model-based diagnosis; OntoDebug; Ontology debugging; Ontology quality assurance; Optimization; RBFS; Recursive best first search; Reiter's hitting set tree; Run time (computers); Search; Search algorithms; Sequential diagnosis; Sound complete best-first diagnosis computation; Fault isolation; Combinatorial search; Fault localization; Knowledge base debugging; Recursive best first search; Hitting set computation; Reiter's hitting set tree; Heuristic search; Sequential diagnosis; Ontology debugging; Linear best-first search; Model-based diagnosis; OntoDebug; Linear best-first hitting set search; HS-tree; Memory-limited diagnosis search; Search; Sound complete best-first diagnosis computation; Diagnosis; RBFS; Ontology quality assurance; Interactive debugging
• ISSN: 0004-3702; 1872-7921; 1872-7921
• DOI: 10.1016/j.artint.2022.103681

Various model-based diagnosis scenarios require the computation of the most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, and to enable successful diagnosis both on memory-restricted devices and for memory-intensive problem cases, we propose two novel diagnostic search algorithms which build upon tried and tested techniques from the heuristic search domain. The first method, dubbed Recursive Best-First Hitting Set Search (RBF-HS), is based on Korf's well-known Recursive Best-First Search (RBFS) algorithm. We show that RBF-HS can enumerate an arbitrary predefined finite number of fault explanations in best-first order within linear space bounds, without sacrificing the desirable soundness or completeness properties. The second algorithm, called Hybrid Best-First Hitting Set Search (HBF-HS), is a hybrid between RBF-HS and Reiter's seminal HS-Tree. The idea is to find a trade-off between runtime optimization and a restricted space consumption that does not exceed the available memory. Notably, both suggested algorithms are generally applicable to any model-based diagnosis problem, regardless of the used (monotonic) logical language to describe the diagnosed system and of the used reasoning mechanism. We conducted extensive experiments on real-world benchmarks from the knowledge-based systems field, a domain where the features soundness, completeness, the best-first property as well as a general applicability are pivotal and where Reiter's HS-Tree is the predominantly used diagnostic search. The evaluation reveals that, when computing fault explanations minimal-cardinality-first, RBF-HS compared to HS-Tree reduces memory requirements substantially in most cases by up to several orders of magnitude, while also saving runtime in more than a third of the cases. When computing fault explanations most-probable-first, RBF-HS compared to HS-Tree tends to trade memory savings more or less one-to-one for runtime overheads. Whenever runtime overheads were significant, using HBF-HS instead of RBF-HS reduced the runtime to values comparable with HS-Tree while keeping the used memory reasonably bounded.