Researchspace >
General science, engineering & technology >
General science, engineering & technology >
General science, engineering & technology >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3958

Title: Emergent future situation awareness: a temporal probabilistic reasoning in the absence of domain experts
Authors: Osunmakinde, I
Bagula, A
Keywords: Dynamic Bayesian networks
Multivariate time series
Temporal probabilistic reasoning
Natural computing algorithms
Artificial intelligence
Hidden markov model
Issue Date: Apr-2009
Publisher: Springer-Verlag Berlin Heidelberg 2009
Citation: Osunmakinde, I and Bagula, A. 2009. Emergent future situation awareness: a temporal probabilistic reasoning in the absence of domain experts. ICANNGA'09: International Conference on Adaptive and Natural Computing Algorithms, Kuopio, Finland, 23-25 April 2009, pp 340-349
Abstract: Dynamic Bayesian networks (DBNs) are temporal probabilistic models for reasoning over time which are rapidly gaining popularity in modern Artificial Intelligence (AI) for planning. A number of Hidden Markov Model (HMM) representations of dynamic Bayesian networks with different characteristics have been developed. However, the varieties of DBNs have obviously opened up challenging problems of how to choose the most suitable model for specific real life applications especially by non-expert practitioners. Problem of convergence over wider time steps is also challenging. Finding solutions to these challenges is difficult. In this paper, we propose a new probabilistic modeling called Emergent Future Situation Awareness (EFSA) which predicts trends over future time steps to mitigate the worries of choosing a DBN model type and avoid convergence problems when predicting over wider time steps. Its prediction strategy is based on the automatic emergence of temporal models over two dimensional (2D) time steps from historical Multivariate Time Series (MTS). Using real life publicly available MTS data on a number of comparative evaluations, our experimental results show that EFSA outperforms popular HMM and logistic regression models. This excellent performance suggests its wider application in research and industries.
Description: Copyright: Springer-Verlag Berlin Heidelberg 2009. This is the authors version of the it is posted here by permission granted by Springer-Verlag. The article is published in the Lecture Notes in Computer Science, Vol.5495(2009), pp 340-349
URI: http://www.springerlink.com/content/321u2518853402p2/
ISBN: 978-3-642-04920-0
Appears in Collections:Advanced mathematical modelling and simulation
Digital intelligence
Mobile intelligent autonomous systems
General science, engineering & technology

Files in This Item:

File Description SizeFormat
Osunmakinde2_2009.pdf94.92 kBAdobe PDFView/Open
View Statistics

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback