Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Bennett:2008:CIMA,

• author = "Andrew Bennett and Derek Magee",
• title = "Using Genetic Programming to Learn Models Containing Temporal Relations from Spatio-Temporal Data",
• booktitle = "Proceedings of the 1st International Workshop on Combinations of Intelligent Methods and Applications",
• year = "2008",
• editor = "Ioannis Hatzilygeroudis and Constantinos Koutsojannis and Vasile Palade",
• address = "Patras, Greece",
• month = jul # " 22",
• organisation = "CEUR",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf",
• URL = "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758",
• URN = "urn:nbn:de:0074-375-1",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• contributor = "CiteSeerX",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.142.8374",
• oai = "oai:CiteSeerXPSU:10.1.1.150.6758",
• abstract = "In this paper we describe a novel technique for learning predictive models from non-deterministic spatio-temporal data. Our technique learns a set of sub-models that model different, typically independent, aspects of the data. By using temporal relations, and implicit feature selection, based on the use of 1st order logic expressions, we make the sub-models general, and robust to irrelevant variations in the data.We use Allen's intervals [1], plus a set of four novel temporal state relations, which relate temporal intervals to the current time. These are added to the system as background knowledge in the form of functions. To combine the sub-models into a single model a context chooser is used. This probabilistically picks the most appropriate set of sub-models to predict in a certain context, and allows the system to predict in non-deterministic situations. The models are learnt using an evolutionary technique called Genetic Programming. The method has been applied to learning the rules of snap, and uno by observation; and predicting a person's course through a network of CCTV cameras.",
• notes = "CIMA'08 Combinations of Intelligent Methods and Applications http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/",

}

Genetic Programming entries for Andrew Bennett Derek Magee

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