Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map. (12th January 2012)
- Record Type:
- Journal Article
- Title:
- Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map. (12th January 2012)
- Main Title:
- Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map
- Authors:
- Suzuki Suzuki, Satoshi Satoshi
Harashima Harashima, Fumio Fumio - Other Names:
- Kenn Kenn Holger Holger Academic Editor.
- Abstract:
- Abstract : 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 differentAbstract : 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. … (more)
- Is Part Of:
- Advances in human-computer interaction. Volume 2012(2012)
- Journal:
- Advances in human-computer interaction
- Issue:
- Volume 2012(2012)
- Issue Display:
- Volume 2012, Issue 2012 (2012)
- Year:
- 2012
- Volume:
- 2012
- Issue:
- 2012
- Issue Sort Value:
- 2012-2012-2012-0000
- Page Start:
- Page End:
- Publication Date:
- 2012-01-12
- Subjects:
- Human-computer interaction -- Periodicals
Human-computer interaction
Periodicals
Electronic journals
004.019 - Journal URLs:
- http://bibpurl.oclc.org/web/50279 ↗
https://www.hindawi.com/journals/ahci/ ↗ - DOI:
- 10.1155/2012/724587 ↗
- Languages:
- English
- ISSNs:
- 1687-5893
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 22464.xml