Context-aware Bayesian intention estimator using Self-Organizing Map and Petri net

For intelligent human-machine systems supporting user's operation, prediction of the user behavior and estimation of one's operational intention are required. However, the same high abilities as human being are required for such intelligent machines since human decides own action using advanced complex recognition ability. Therefore, the present authors proposed a Bayesian intention estimator using Self-Organizing Map (SOM). This estimator utilizes a mapping-relation obtained using SOM to find transition of the intentions. In this paper, an improvement of the Bayesian intention estimator is reported by considering the task context. The scenario of whole task is modeled by Petri net, and prediction of belief in Bayesian computation is modified by other probability estimated from the Petri-Net scenario. Applying the presented method to an estimation problem using a remote operation of the radio controlled construction equipments, improvements of the estimator were confirmed; an undetected intention modes were correctly detected, and inadequate identification was corrected with adequate timing.