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Found 21 papers, 0 papers with code
Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging
For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair.
How should I compute my candidates? A taxonomy and classification of diagnosis computation algorithms
This work proposes a taxonomy for diagnosis computation methods which allows their standardized assessment, classification and comparison.
DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential Diagnosis
The computation of this sample can be accomplished by various diagnostic search algorithms.
RBF-HS: Recursive Best-First Hitting Set Search
For the computation of minimum-cardinality fault explanations, we find that (1) RBF-HS reduces memory requirements substantially in most cases by up to several orders of magnitude, (2) in more than a third of the cases, both memory savings and runtime savings are achieved, and (3) given the runtime overhead is significant, using HBF-HS instead of RBF-HS reduces the runtime to values comparable with HS-Tree while keeping the used memory reasonably bounded.
Do We Really Sample Right In Model-Based Diagnosis?
Statistical samples, in order to be representative, have to be drawn from a population in a random and unbiased way.
Sound, Complete, Linear-Space, Best-First Diagnosis Search
Various model-based diagnosis scenarios require the computation of the most preferred fault explanations.
The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis Approach
A common issue for companies is that the volume of product orders may at times exceed the production capacity.
On Expert Behaviors and Question Types for Efficient Query-Based Ontology Fault Localization
We challenge existing query-based ontology fault localization methods wrt.
Understanding the QuickXPlain Algorithm: Simple Explanation and Formal Proof
In his seminal paper of 2004, Ulrich Junker proposed the QuickXPlain algorithm, which provides a divide-and-conquer computation strategy to find within a given set an irreducible subset with a particular (monotone) property.
Towards Optimizing Reiter's HS-Tree for Sequential Diagnosis
Reiter's HS-Tree is one of the most popular diagnostic search algorithms due to its desirable properties and general applicability.
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success.
A New Expert Questioning Approach to More Efficient Fault Localization in Ontologies
As a remedy, we suggest a new type of expert question which aims at fitting the answering behavior of all analyzed experts.
Evaluating Active Learning Heuristics for Sequential Diagnosis
As initial system observations usually do not suffice to deterministically pin down just one explanation of the system's misbehavior, additional system measurements can help to differentiate between possible explanations.
A Generally Applicable, Highly Scalable Measurement Computation and Optimization Approach to Sequential Model-Based Diagnosis
To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements.
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search
Based on these, we demonstrate how efficient heuristic search methods for optimal queries in query synthesis AL scenarios can be devised.
Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis
In this work we present strategies for (optimal) measurement selection in model-based sequential diagnosis.
Scalable Computation of Optimized Queries for Sequential Diagnosis
During query computation, existing sequential diagnosis methods often require the generation of many unnecessary query candidates and strongly rely on expensive logical reasoners.
A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics
A broad variety of knowledge-based applications such as recommender, expert, planning or configuration systems usually operate on the basis of knowledge represented by means of some logical language.
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging
The most essential benefit of such logical KBs is the opportunity to perform automatic reasoning which however requires a KB to meet some minimal quality criteria such as consistency.
Interactive Debugging of Knowledge Bases
Even if a system is complete and sound and considers only minimal solutions, there are generally exponentially many solution candidates to select one from.
Interactive ontology debugging: two query strategies for efficient fault localization
The latter allows knowledge about typical user errors to be exploited to minimize the number of queries.
