Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

Rodler, Patrick; Schmid, Wolfgang P.; Schekotihin, Konstantin

doi:10.29007/vd18

Cited by 5 publications

(11 citation statements)

References 20 publications

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“…Our work is related to sequential diagnosis , which is a diagnosis problem in which observations are generated and given the diagnostic system sequentially. Most previous work on sequential diagnosis focused on which observation to generate to reduce the number of diagnoses [ 9 , 30 , 31 , 32 , 33 ], or to learn more about the probabilities of components to be faulty [ 34 ]. Our work can be used by sequential diagnosis algorithm to allow them to process faster the sequence of observations they receive.…”

Section: Background and Related Workmentioning

confidence: 99%

Minimal Cardinality Diagnosis in Problems with Multiple Observations

2021

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Model-Based Diagnosis (MBD) is a well-known approach to diagnosis in medical domains. In this approach, the behavior of a system is modeled and used to identify faulty components, i.e., once a symptom of abnormal behavior is observed, an inference algorithm is run on the system model and returns possible explanations. Such explanations are referred to as diagnoses. A diagnosis is an assumption about which set of components are faulty and have caused the abnormal behavior. In this work, we focus on the case where multiple observations are available to the diagnoser, collected at different times, such that some of these observations exhibit symptoms of abnormal behavior. MBD with multiple observations is challenging because some components may fail intermittently, i.e., behave abnormally in one observation and behave normally in another, while other components may fail all the time (non-intermittently). Inspired by recent success in solving classical diagnosis problems using Boolean satisfiability (SAT) solvers, we describe two SAT-based approaches to solve this MBD with multiple observations problem. The first approach compiles the problem to a single SAT formula, and the second approach solves each observation independently and then merges them together. We compare these two approaches experimentally on a standard diagnosis benchmark and analyze their pros and cons.

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Section: Background and Related Workmentioning

confidence: 99%

Minimal Cardinality Diagnosis in Problems with Multiple Observations

2021

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“…Such a quality measure can, for example, be based on the expected information gain after the user has answered the query [21], on reinforcement learning [80], or on criteria [53,81,82] adopted from the field of active learning [83]. Finally, since the generation of queries requires potentially costly calls to an underlying reasoner, approaches exist that aim to minimize the number of these computations [20,67,84,53].…”

Section: Definition 3 (Oracle) Letmentioning

confidence: 99%

“…Hence, using methods in a query-based debugger that are able to generate "easy questions" with respect to such a prediction model is expected to be beneficial to avoid oracle errors. Examples of such methods can be found in [52,84,78].…”

Section: Evaluation Of the Prediction Model (Study E2)mentioning

confidence: 99%

Are query-based ontology debuggers really helping knowledge engineers?

Rodler

Jannach

Schekotihin

et al. 2019

Knowledge-Based Systems

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Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontology-based systems, were proposed throughout recent years. Query-based debugging is a comparably recent interactive approach that localizes the true cause of an observed problem by asking knowledge engineers a series of questions. Concrete implementations of this approach exist, such as the OntoDebug plug-in for the ontology editor Protégé.To validate that a newly proposed method is favorable over an existing one, researchers often rely on simulation-based comparisons. Such an evaluation approach however has certain limitations and often cannot fully inform us about a method's true usefulness. We therefore conducted different user studies to assess the practical value of query-based ontology debugging. One main insight from the studies is that the considered interactive approach is indeed more efficient than an alternative algorithmic debugging based on test cases. We also observed that users frequently made errors in the process, which highlights the importance of a careful design of the queries that users need to answer.

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“…To test the proposed query synthesis strategy, we adopted the same evaluation setting on 8 real-world model-based diagnosis problems (MBD-Ps) as reported in [34]. In particular, we performed 5 query synthesis runs for each combination of MBD-P and |V | ∈ {10, 20, .…”

Section: Query Synthesis In Sequential Diagnosismentioning

confidence: 99%

“…For these reasons, query synthesis in principle appears to be a promising solution especially in model-based applications as it attempts to actually compute a minimal number of queries and associated QSM-values until a (sufficiently) good one is found. The viability and benefit of one query synthesis method to model-based SD has been recently shown in [34].…”

Section: Introductionmentioning

confidence: 99%

On Active Learning Strategies for Sequential Diagnosis

Rodler¹

Kalpa Publications in Computing

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When diagnosing a faulty system one is often confronted with a large number of possible fault hypotheses. Sequential Diagnosis (SD) techniques aim at the localization or identification of the actual fault with minimal cost or effort. SD can be viewed as an Active Learning (AL) task where the learner, trying to find some target hypothesis, formulates sequential queries to some oracle, thereby e.g. requesting additional system measurements. Several query selection measures (QSMs) for determining the best query to ask next have been proposed for AL. To date, few of them have been translated to and employed in SD. In this work, we account for this and analyze various QSMs wrt. to the discrimination power of their selected queries within the diagnostic hypotheses space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in SD to choose a query from a pool of candidates. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we devise an efficient heuristic search for optimal queries. As (preliminary) evaluation results indicate, the latter is especially beneficial in applications where query generation is costly, e.g. involving logical reasoning, and hence a pool of query candidates is not (cheaply) available.

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Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

Cited by 5 publications

References 20 publications

Minimal Cardinality Diagnosis in Problems with Multiple Observations

Minimal Cardinality Diagnosis in Problems with Multiple Observations

Are query-based ontology debuggers really helping knowledge engineers?

On Active Learning Strategies for Sequential Diagnosis

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