Alle Publikationen
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2004
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(2004): Fuzzy Reasoning in a Multiagent System of Surveillance Sensors to Manage Cooperatively the Sensor-to-Task Assignment Problem. In: Applied Artificial Intelligence 18 (8), S. 673-711. DOI: 10.1080/08839510490496789
DOI: https://doi.org/10.1080/08839510490496789 Abstract: In this work, a surveillance network composed of a set of sensors and a fusion center is designed as a multiagent system. Negotiation among sensors (agents) is proposed to solve the task-to-sensor assignment problem (the allocation of tasks to sensors), addressing several aspects. First, the fusion center determines the tasks (system tasks) to be performed by the network at each management cycle. To do that, a fuzzy reasoning system determines the priorities of these system tasks by means of a symbolic inference process using the fused data received from all sensors. In addition, a fuzzy reasoning process, similar to that performed in the fusion center, is proposed to evaluate the priority of local tasks (sensor tasks) now executed by each sensor. The network coordination procedure will be based on the system-task priorities, computed in the fusion center, and on the local priorities evaluated in each sensor. Priority values for system and sensor tasks will be the basis to guide a negotiation process among sensors in the multiagent system. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the negotiation scheme, based on task priority and measured through their time-variant priority, allows the adaption of sensor operation to changing situations. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
1993
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(1993): A fuzzy linguistic approach generalizing Boolean Information Retrieval. A model and its evaluation. In: Journal of the American Society for Information Science 44 (2), S. 70-82. DOI: 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I
Abstract: The generalization of Boolean information Retrieval Systems (IRS) is still an open research field; In fact, though such systems are diffused on the market, they present some limitations; one of the main features lacking in these systems is the ability to deal with the ''Imprecision'' and ''subjectivity'' characterizing retrieval activity. However, the replacement of such systems would be much more costly then their evolution through the incorporation of new, features to enhance their efficiency and effectiveness. Previous efforts in this ares have led to the introduction of numeric weights to improve both document representation and query language. By attaching a numeric weight to a term in a query, a user can provide a quantitative description of the ''importance'' of that term in the documents he or she is looking for. However, the use of weights requires a clear knowledge of their semantics for translating a fuzzy concept into a precise numeric value. Our acquaintance with these problems led us to define, starting from an existing weighted Boolean retrieval model, a linguistic extension, formalized within fuzzy set theory, in which numeric query weights are replaced by linguistic descriptors which specify the degree of importance of the terms. This fuzzy linguistic model is defined and an evaluation is made of its implementation on a Boolean IRS.
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