Abstract
According to complexity science, the essence of a complex system is the emergence of unpredictable behavior from interaction among components. Loosely inspired by this idea, a diagnosis technique of a class of discrete-event systems, called complex active systems, is presented. A complex active system is a hierarchical graph, where each node is a network of communicating automata, called an active unit. Specific interaction patterns among automata within an active unit give rise to the occurrence of emergent events, which may affect the behavior of superior active units. This results in the stratification of the behavior of the complex active system, where each different stratum corresponds to a different abstraction level of the emergent behavior. As such, emergence is a peculiar property of a complex active system. To speed up the diagnosis task, model-based knowledge is compiled offline and exploited online by the diagnosis engine. The technique is sound and complete.
Highlights
In an interview in January 2000, the physicist Stephen Hawking was asked the following question [32]: Some say that while the 20th century was the century of physics, we are entering the century of biology
The states are identified by numbers, the final states are double circled, and the transitions are marked by relevant information only, namely: observable label, fault, occurrence of an emergent event, and consumption of an event emerged from a child unit
The contributions of this paper are the specification of a class of discrete event systems (DESs) inspired by the complexity paradigm, called complex active systems, and a knowledge-compilation technique that speeds up online diagnosis for such systems
Summary
In an interview in January 2000, the physicist Stephen Hawking was asked the following question [32]: Some say that while the 20th century was the century of physics, we are entering the century of biology. Inspired by complexity science, this paper presents a method to extract knowledge - above all, about emergent behavior - from the models of individual clusters of (component) systems and to exploit this knowledge for the lazy diagnosis of a class of discrete event systems (DESs) [5], called complex active systems (CASs). Despite the fact that the considered systems are static and the diagnosis method is consistency-based, neither complexity nor emergent behavior is conceived, as the goal of the technique is efficiency of the diagnosis task by exploitation of the structure of the system. A variant of the decentralized / distributed approach to diagnosis of DESs is introduced in [16], with the aim of computing local diagnoses which are globally consistent To this end, as in [28] but in the different perspective of diagnosis computation rather than diagnosability, a technique based on jointrees (called junction trees in the paper) is proposed. The contribution of the present paper is threefold: (a) specification of a CAS based on active units, (b) proposal of a process for extracting knowledge from active units, and (c) specification of a diagnosis task for CASs exploiting compiled knowledge
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