Bayesian intention estimator using Self-Organizing Map and its experimental verification

Model of a human-machine system to express the process of cognition-intention-action is presented by referring a concept of an attention in Global Workspace Theory. Based on the model, new approach to estimate intentions of an operator who manipulates machines is proposed by utilizing Self-Organizing Map (SOM) and the Bayes filtering. Probabilities of transition of intentions are approximated by SOM from the operational log data, and the intentions are estimated through a Bayesian particle filtering with the trained SOM. The effectiveness was verified by applying to the remote operational task, and potential of the intention estimator was confirmed. Several issues were analyzed, and guideline for further improvement is discussed.