Paper Info
Title
Estimation of operational intentions utilizing Self-Organizing Map with Bayes filtering
Abstract
An estimation algorithm of operational intentions in the machine operation is presented in this paper. State transition relation of intentions was formed using Self-Organizing Map (SOM) from the measured data of the operation and environmental variables with the reference intention sequence. Operational intention was estimated by stochastic computation using a Bayesian particle filter with the trained SOM. The presented algorithm was applied to the remote operational task, and qualitative and quantitative analyses were performed. As a result, it was confirmed that the estimator could classify the types of intentions as similarly as the human analyst discerned. Further, several issues, such as difficulty in preparation of objective normative data, and necessity of consideration of scenario / causality, are discussed.
Year
DOI
Venue
2010
10.1109/IROS.2010.5651157
IROS
Keywords
Field
DocType
particle filtering (numerical methods),stochastic processes,man-machine systems,human computer interaction,reference intention sequence,machine operation,bayes methods,intention classification,pattern classification,human intention estimation,data analysis,som,self-organizing map,estimation theory,operational intention estimation,stochastic computation,remote consoles,remote operational task,self-organising feature maps,bayesian particle filter,particle filter,prediction algorithms,self organizing map,estimation,state transition
Computer vision,Computer science,Particle filter,Filter (signal processing),Stochastic process,Self-organizing map,Artificial intelligence,Estimation theory,Machine learning,Bayesian probability,Bayes' theorem,Estimator
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4244-6674-0
4
PageRank 
References 
Authors
0.56
6
2
Name
Order
Citations
PageRank
Satoshi Suzuki1123.49
Fumio Harashima216825.48