Estimation of operational intentions utilizing Self-Organizing Map with Bayes filtering

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.

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