Paper Info
Title
Estimation algorithm of machine operational intention by bayes filtering with self-organizing map
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 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.
Year
DOI
Venue
2012
10.1155/2012/724587
Adv. Human-Computer Interaction
Keywords
Field
DocType
dominant intention mode,intentions pattern,intention mode,multiple different intention,normal intention mode,operational intention mode,reference intention sequence,machine operational intention,percent concordance ratio,self-organizing map,intention estimator algorithm,human analyst,estimation algorithm
Data mining,Computer science,Particle filter,Self-organizing map,Artificial intelligence,Computation,Bayes' theorem,Remote operation,Simulation,Algorithm,Filter (signal processing),Machine learning,Estimator,Bayesian probability
Journal
Volume
ISSN
Citations 
2012,
1687-5893
3
PageRank 
References 
Authors
0.43
14
2
Name
Order
Citations
PageRank
Satoshi Suzuki1123.49
Fumio Harashima216825.48