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Sequential model-based diagnosis by systematic search

ARTIFICIAL INTELLIGENCE 2023年第1期323卷

作者： Rodler, Patrick Univ Klagenfurt Univ Str 65-67 Klagenfurt Austria

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions. As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain. To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided. As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with

关键词： Model-based diagnosis Sequential diagnosis query-based debugging Interactive debugging Combinatorial search Diagnostic decision-making Fault localization Measurement selection Knowledge-base debugging Ontologies query computation Active learning Heuristics

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One step at a time: An efficient approach to query-based ontology debugging

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KNOWLEDGE-BASED SYSTEMS 2022年 251卷

作者： Rodler, Patrick Univ Klagenfurt Univ Str 65-67 Klagenfurt Austria

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for a semi-automatic fault localization in ontologies have been proposed and extensively studied. One class of these approaches involve a human expert who provides answers to system-generated queries about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting expert. In this work, we demonstrate that these assumptions might not always be adequate and discuss consequences of their violations. In particular, we characterize a range of expert types with different query answering behavior and show that existing approaches are far from achieving optimal efficiency for all of them. In addition, we find that the cost metric adopted by state-of-the-art techniques might not always be realistic and that a change of metric has a decisive impact on the best choice of query answering strategy. As a remedy, we suggest a new - and simpler - type of expert question that leads to a stable fault localization performance for all analyzed expert types and effort metrics, and has numerous further advantages over existing techniques. Moreover, we present an algorithm which computes and optimizes this new query type in worst-case polynomial time and which is fully compatible with existing concepts (e.g., query selection heuristics) and infrastructure (e.g., debugging user interfaces) in the field. Comprehensive experiments on faulty real-world ontologies attest that the new querying method is substantially and statistically significantly superior to existing techniques b

关键词： Ontology debugging query -based ontology debugging Interactive debugging Fault localization Sequential diagnosis Expert questions Ontology quality assurance Ontology repair Test-driven debugging Singleton query Model -based diagnosis Semantic Web User interaction Effort minimization Performance optimization Entailment Test cases query computation query selection One-step lookahead Debugging cost criteria query quality assessment Expert types Heuristics Knowledge -base debugging OntoDebug Protege

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