Automated diagnosis of communicating-automaton networks (CANs) is a complex task, which is typically faced by model-based reasoning, where the behavior of the network is reconstructed based on its observation. This task may take advantage of knowledge-compilation techniques, where a large amount of reasoning is anticipated off-line (when the diagnostic process is not active), by simulating the behavior of the network and by constructing suitable data structures embedding diagnostic information. This (general-purpose) compiled knowledge is exploited on-line (when the diagnostic process becomes active), so as to generate the solution to the problem. Additional reusable (special-purpose) compiled knowledge is generated on-line when solving new problems. A software environment for the diagnosis of CANs has been developed in the C programming language with the support of the PostgreSQL relational database management system, under the Linux operating system. It supports the modeling and preprocessing of CANs as well as the solution of diagnostic problems, including on-line knowledge compilation. The environment has been tested through a variety of experiments. Results are encouraging and provide a valuable feedback for further work. 366S. CERUTTI ET AL. most real-world systems can be viewed as CANs and reasoning about such networks is easier than about continuous systems, from the middle of the 1990s the task of diagnosis of CANs has been receiving an increasing amount of interest from both the artificial intelligence [3][4][5][6][7][8] and the automatic control communities [9][10][11][12][13][14][15][16].Diagnosing a network means computing its candidate diagnoses, each of which is a set of faults that explains the observation collected during the network operation. In the general case, the specific faults of a network cannot be inferred without finding out what has happened to the system [17]. In this way, the system evolutions complying with the observation, called the histories [18], situation histories or narratives [19], paths [20], or trajectories [21], become a product of the diagnostic reasoning.Determining the system evolutions is computationally expensive (see [22] for the difficulties of the diagnoser approach [9,10], or the worst-case computational complexity analysis in [18], or the discussion in [23]). This is why most approaches exploit a trade-off between off-line and on-line computation: some kind of knowledge, implicit in the models of the structure and behavior of the network, is compiled off-line in order to speed up on-line processing.While the approaches by other authors deal with networks wherein the communication between automata is supported by synchronous links, the CANs taken into account here and in previous works by the present authors constitute an adaptation of communicating finite-state machines [24,25], these being networks of finite-state machines that asynchronously exchange messages with each other through FIFO links. In particular, this paper presents a diagnostic environment for a...
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