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By Dan Lawesson.

ISBN-10: 9185457396

ISBN-13: 9789185457397

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1. 1, a bus, a server and a client. For brevity we call the bus component b, the server is called s and we use c to denote the client component. We also use the convention of describing mappings as sets. The initial system state is thus σ0 = { c → (init, ∅), s → (ready, ∅), b → (wait, ∅) } Mathematical Foundation 39 First assume that the bus does not enter its down state. Then the first step of this system is for the client and server to synchronize with a request. The system ends up in state σ1 as follows.

In this thesis, however, we describe a DX approach. See Cordier et al [CDL+ 04] for a more elaborate investigation of similarities and differences of the two approaches to diagnosis. We first introduce the concept of discrete diagnosis with structural system models, an instance of model-based diagnosis. We then further discuss various implications of introducing more powerful models such as behavioral models. 1 17 The Discrete Diagnosis Problem Pioneered by Reiter [Rei87], the area of discrete diagnosis is an instance of model-based diagnosis.

The set of all runs of system SD is denoted runs(SD). ✷ We would like to be able to express substitution of components in a system, and in the following definition we define replacement of the components in the set C1 by the component c2 . 2. Let SD be a system description, C1 a non-empty set of components and c2 a component. Then SD [C1 ⇒ c2 ] = (SD − C1 ) ∪ { c2 }. ✷ The logged and critical events that have taken place upto a certain system state σ in a system SD is denoted seen(σ) and defined as follows.

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An approach to diagnosability analysis for interacting finite state systems by Dan Lawesson.

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