Predicting users' requests on the WWW
UM '99 Proceedings of the seventh international conference on User modeling
Designing Sociable Robots
Self-Organizing Maps
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multiphase Learning for an Interval-Based Hybrid Dynamical System
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Human-Computer Interaction
Segmentation and analysis of console operation using self-organizing map with cluster growing method
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
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.