Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Hierarchical model-based diagnosis
International Journal of Man-Machine Studies
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Diagnosis of discrete-event systems from uncertain temporal observations
Artificial Intelligence
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
A bridged diagnostic method for the monitoring of polymorphic discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model-Based Diagnosis of Discrete Event Systems with an Incomplete System Model
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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Observations play a major role in Model-Based Reasoning. In an uncertain, event-driven perspective, the observation of a dynamical system over a time interval is not perceived as a totally-ordered sequence of observable labels but, rather, as a directed acyclic graph. Problem solving, however, requires generating a surrogate of such a graph, the index space. In addition, when tasks such as monitoring and diagnosis are carried out, the observation hypothesized so far has to be integrated at the reception of a new fragment of observation. This translates to the need for computing a new index space every time. Since such a computation is expensive, a naive generation of the index space from scratch at the occurrence of each observation fragment becomes prohibitive in real applications. To cope with this problem, the paper introduces an incremental technique for efficiently modeling and indexing temporal observations.