Diagnosis of discrete-event systems from uncertain temporal observations

Lamperti, Gianfranco; Zanella, Marina

doi:10.1016/s0004-3702(02)00123-6

Cited by 51 publications

(49 citation statements)

References 11 publications

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“…While the Diagnoser approach compiles the diagnostic model (i.e., the Diagnoser itself) of the whole system off-line, other approaches (see e.g., Lamperti & Zanella, 2002;Cordier & Grastien, 2007) compute all possible system behaviors, and check which of these behaviors are correct. Grastien, Haslum, and Thiébaux (2012) extends to DESs the conflict-based approach initially proposed by Reiter (1987) on static systems.…”

Section: Discrete-event Systemsmentioning

confidence: 99%

Cooperative Monitoring to Diagnose Multiagent Plans

Micalizio¹,

Torasso²

2014

jair

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Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are independent of each other.In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other.The paper presents a novel methodology, named Cooperative Weak-Committed Monitoring (CWCM), enabling agents to cooperate while monitoring their own actions. Cooperation helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondeterministic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures.An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced.

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Section: Discrete-event Systemsmentioning

confidence: 99%

Cooperative Monitoring to Diagnose Multiagent Plans

Micalizio¹,

Torasso²

2014

jair

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“…This causes a distortion of the trace, where each label is perceived as a set of candidate labels, while the total temporal ordering among labels is relaxed to partial temporal ordering. The result is an uncertain temporal observation [11], which is represented by a directed acyclic graph, where nodes are marked by candidate labels, while edges define partial temporal ordering among nodes.…”

Section: Originating Application Domainmentioning

confidence: 99%

Metrics-Based Incremental Determinization of Finite Automata

Balan

Lamperti

Scandale

2014

Advanced Information Systems Engineering

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Abstract. Some application domains, including monitoring of active systems in artificial intelligence and model-based mutation testing in software engineering, require determinization of finite automata to be performed incrementally. To this end, an algorithm called Incremental Subset Construction (ISC) was proposed a few years ago. However, this algorithm was recently discovered to be incorrect is some instance problems. The incorrect behavior of ISC originates when the redirection of a transition causes a portion of the automaton to be disconnected from the initial state. This misbehavior is disturbing in two ways: portions of the resulting automaton are disconnected and, as such, useless; moreover, a considerable amount of computation is possibly wasted for processing these disconnected parts. To make ISC sound, a metrics-based technique is proposed in this paper, where the distance between states is exploited in order to guarantee the connection of the automaton, thereby allowing ISC to achieve soundness. Experimental results show that, besides being effective, the proposed technique is efficient too.

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“…Temporal models are more complex than atemporal ones [6,7]. Even when they involve a few variables, in a temporal model each variable and its relationships with other variables must be examined over multiple points of time.…”

Section: Introductionmentioning

confidence: 99%

Temporal Bayesian Network of Events for Diagnosis and Prediction in Dynamic Domains

Figueroa

Sucar

2005

Appl Intell

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In some domains like industry, medicine, communications, speech recognition, planning, tutoring systems, and forecasting; the timing of observations (symptoms, measures, test, events, as well as faults) play a major role in diagnosis and prediction. This paper introduces a new formalism to deal with uncertainty and time using Bayesian networks called Temporal Bayesian Network of Events (TBNE). In a TBNE each node represents an event or state change of a variable, and an arc corresponds to a causal-temporal relationship. A temporal node represents the time that a variable changes state, including an option of no-change. The temporal intervals can differ in number and size for each temporal node, so this allows multiple granularity. Our approach is contrasted with a Dynamic Bayesian network for a simple medical example. An empirical evaluation is presented for a subsystem of a thermal power plant, in which this approach is used for fault diagnosis and event prediction with good results. The TBNE model can be used for the diagnosis of a cascade of anomalies arising with certain delays; this situation is typical in the diagnosis of medical and industrial processes.

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Diagnosis of discrete-event systems from uncertain temporal observations

Cited by 51 publications

References 11 publications

Cooperative Monitoring to Diagnose Multiagent Plans

Cooperative Monitoring to Diagnose Multiagent Plans

Metrics-Based Incremental Determinization of Finite Automata

Temporal Bayesian Network of Events for Diagnosis and Prediction in Dynamic Domains

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