1,721,032 research outputs found
Sensor data validation for nuclear power plants through Bayesian conditioning and Dempster's rule of combination
No full textSensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nuclear power plant. In particular, they become critical in case of conflicts among the data. If the monitored system's description model is correct and its components work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors. This paper introduces and discusses three simple ideas: 1. classical model-based diagnosis can be extended straightforwardly to encompass the sensors' models into the system's description in order to diagnose even their own faults 2. from the log-file of the diagnosed minimal conflicts among the sensors, one can draw interesting conclusion regarding their relative reliability (e.g., through Bayesian conditioning) 3. the estimated reliability of the sensors is useful when assessing (e.g., through Dempster's Rule of Combination) the actual state of the monitored physical system, even in cases of conflicting data. These ideas lead to the conception of a distributed monitoring system able to attach to each sensor a statistically evaluated relative reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants
Distributed Belief Revision
No full textIn this paper, a distributed approach to belief revision is presented. It is conceived as a collective activity of a group of interacting agents, in which each component contributes with its own local beliefs. The integration of the different opinions is performed not by an external supervisor, but by the entire group through an election mechanism. Each agent exchanges information with the other components and uses a local belief revision mechanism to maintain its cognitive state consistent. We propose a model for local belief revision/integration based on what we called: ldquoPrinciple of Recoverability.rdquo Computationally, our way to belief revision consists of three steps acting on the symbolic part of the information, so as to deal with consistency and derivation, and two other steps working with the numerical weight of the information, so as to deal with uncertainty. In order to evaluate and compare the characteristics and performance of the centralized and of the distributed approaches, we made five different experiments simulating a simple society in which each agent is characterized by a degree of competence, communicates with some others, and revise its cognitive state. The results of these experiments are presented in the paper
Belief revision through the belief-function formalism in a multi-agent environment
No full textThe abilities of detecting contradictions and rearranging the cognitive space in order to cope with them are important to be embedded in the BDI architecture of an agent acting in a complex and dynamic world. However, to be accomplished in a multi-agent environment, ldquobelief revisionrdquo must depart considerably from its original definitions. According to us, the main changes should be the following ones:
1. replacing the ldquopriority to the incoming information principlerdquo with the ldquorecoverability principlerdquo: any previously believed piece of information must belong to the current cognitive state whenever it is possible
2. dealing not just with pieces of information but with couples since the reliability of the source affects the credibility of the information and vice-versa.
The ldquobelief-functionrdquo formalism is here accepted as a simple and intuitive way to transfer the sources' reliability to the information's credibility
Distributed knowledge revision/integration
No full textWe propose a distributed architecture for knowledge revision-integration, where each element is conceived as a knowledge-based system able to exchange information with the others. Since nodes can be affected by some degree of incompetence, part of the information running through the network may be incorrect and might cause contradictions in the knowledge base of some nodes. To manage these contradictions, each node is equipped with a belief revision module which makes it able to discriminate among more or less credible information and more or less reliable information sources. Our aim is that of comparing on a simulation basis the performances and the characteristics of this distributed system vs. those of a centralized architecture. We report here the first results of our experiments
Distributed Knowledge Elicitation through the Dempster-Shafer theory of evidence: a simulation study
Full text linkRevising Beliefs in a Multi-Source Environment
No full textSince the seminal, philosophical and influential works of Alchourr'on, Gardenfors and Makinson, ideas on "belief revision" have been progressively refined toward normative, effective and computable paradigms. Side by side to this "symbolic" line of research, there has been also a "numerical" approach to belief revision whose main contributes were the probabilistic and the evidence-based approaches. The opinion expressed in this paper is that, to be applied in a multi-sources environment, belief revision has to depart considerably from the original framework. In particular, it has to abandon the fundamental principle of "Priority to the Incoming Information" in preference to what we called the principle of "Recoverability". Furthermore the semantic approach should be blended with a syntactic treatment of consistency inspired by the Truth Maintenance System
Sistemi di Monitoraggio Distribuiti Automonitoranti
No full textLa fusione e l’interpretazione di dati provenienti da sensori, sono
operazioni critiche in caso di conflitto. Se il modello descrittivo del sistema
monitorato è corretto ed i componenti lavorano correttamente, allora le
incompatibilità tra i dati possono essere imputate solo al deterioramento, o a
rotture permanenti, di uno o più sensori.
L’articolo introduce e discute tre semplice idee:
1. La “Model-Based Diagnosis” classica può essere estesa direttamente per
incorporare il modello dei sensori nella descrizione del sistema, in modo
tale da diagnosticare anche i malfunzionamenti degli stessi (sensors’
faults)
2. dall’analisi storica dei conflitti minimali diagnosticati tra i sensori, si
possono trarre interessanti conclusioni riguardanti la loro attendibilità
relativa (attraverso il Condizionamento Bayesiano)
3. l’attendibilità stimata dei sensori è utile per valutare (attraverso la Regola
di Combinazione di Dempster) lo stato attuale del sistema fisico
monitorato, anche in caso di dati conflittuali e non ridondanti
Distributed Knowledge Elicitation under limited degrees of competence: a simulation study
No full textA self-diagnosing distributed monitoring system for nuclear power plants
No full textSensor data fusion and interpretation, sensor failure detection, isolation and identification are extremely important activities for the safety of a nuclear power plant. In particular, they become critical in cases of conflicts among the data. If the monitored system's description model is correct and its components work properly, then incompatibilities among data may only be attributed to temporary deterioration or permanent breakage of one or more sensors. This paper introduces and discusses the conception of a distributed monitoring system able to attach each sensor with a statistically-evaluated relative degree of reliability, which is especially useful for devices situated in dangerous zones or areas, difficult to reach inside huge and complex power plants
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