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Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map
Joint Authors
Suzuki, Satoshi
Harashima, Fumio
Source
Advances in Human-Computer Interaction
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-01-12
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system.
In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM) from the measured data of the operation and environmental variables with the reference intention sequence.
The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trained SOM.
This method enables to omit the troublesome process to specify types of information which should be used to build the estimator.
Applying the proposed method to the remote operation task, the estimator's behavior was analyzed, the pros and cons of the method were investigated, and ways for the improvement were discussed.
As a result, it was confirmed that the estimator can identify the intention modes at 44–94 percent concordance ratios against normal intention modes whose periods can be found by about 70 percent of members of human analysts.
On the other hand, it was found that human analysts' discrimination which was used as canonical data for validation differed depending on difference of intention modes.
Specifically, an investigation of intentions pattern discriminated by eight analysts showed that the estimator could not identify the same modes that human analysts could not discriminate.
And, in the analysis of the multiple different intentions, it was found that the estimator could identify the same type of intention modes to human-discriminated ones as well as 62–73 percent when the first and second dominant intention modes were considered.
American Psychological Association (APA)
Suzuki, Satoshi& Harashima, Fumio. 2012. Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map. Advances in Human-Computer Interaction،Vol. 2012, no. 2012, pp.1-20.
https://search.emarefa.net/detail/BIM-493558
Modern Language Association (MLA)
Suzuki, Satoshi& Harashima, Fumio. Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map. Advances in Human-Computer Interaction No. 2012 (2012), pp.1-20.
https://search.emarefa.net/detail/BIM-493558
American Medical Association (AMA)
Suzuki, Satoshi& Harashima, Fumio. Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map. Advances in Human-Computer Interaction. 2012. Vol. 2012, no. 2012, pp.1-20.
https://search.emarefa.net/detail/BIM-493558
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-493558