Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Diagnosis of large active systems
Artificial Intelligence
Model checking
Diagnosis of discrete-event systems from uncertain temporal observations
Artificial Intelligence
Coordinated Decentralized Protocols for Failure Diagnosisof Discrete Event Systems
Discrete Event Dynamic Systems
Discrete Event Dynamic Systems
Process algebras for systems diagnosis
Artificial Intelligence
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
All from One, One for All: on Model Checking Using Representatives
CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Linear Time, Branching Time and Partial Order in Logics and Models for Concurrency, School/Workshop
Situation recognition: representation and algorithms
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Diagnosis of a class of distributed discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Using DESs for Temporal Diagnosis of Multi-agent Plan Execution
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Diagnosis of Plan Structure Violations
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
Plan Diagnosis and Agent Diagnosis in Multi-agent Systems
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A Test Theory of the Model-Based Diagnosis
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Robust codiagnosability of discrete event systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Observer for an omnidirectional mobile robot
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Maximal-confirmation diagnoses
Knowledge-Based Systems
Active fault tolerant control of discrete event systems using online diagnostics
Automatica (Journal of IFAC)
Behavioural Proximity Discovery: an adaptive approach for root cause analysis
International Journal of Business Intelligence and Data Mining
Diagnosis of plan execution and the executing agent
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
Context-sensitive diagnosis of discrete-event systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Embedded holonic fault diagnosis of complex transportation systems
Engineering Applications of Artificial Intelligence
Robust diagnosis of discrete-event systems against permanent loss of observations
Automatica (Journal of IFAC)
Multi-agent epistemic explanatory diagnosis via reasoning about actions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Reasoning on partially-ordered observations in online diagnosis of DESs
AI Communications
An event-based distributed diagnosis framework using structural model decomposition
Artificial Intelligence
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We address the problem of diagnosing large discrete event systems. Given a flow of observations from the system, the goal is to explain these observations on-line by identifying and localising possible failures and their consequences across the system. Model-based diagnosis approaches deal with this problem but, apart very recent proposals, either they require the computation of a global model of the system which is not possible with large discrete event systems, or they cannot perform on-line diagnosis. The contribution of this paper is the description and the implementation of a formal framework for the on-line decentralised diagnosis of such systems, framework which is based on the ''divide and conquer'' principle and does not require the global model computation. This paper finally describes the use of this framework in the monitoring of a real telecommunication network.