研究概要

Artificial Intelligence

We develop various machine learning techniques and apply them to several human-in-the-loop systems.

1. Driving Support System using Double Articulation Analyzer

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We don’t randomly drive a car, we drive it in some organized way. Humans drive cars while referring to some driving contexts and output some well-organized sequence of driving behaviors to adequately move along a street. We are trying to develop an unsupervised machine learning technique, called the double articulation analyzer, which can extract meaningful segments of driving behavior. In addition, we intend to develop several advanced driving assistance systems in the future for creating an exciting symbiosis between humans and machines.

** This research is mainly a collaborative research with DENSO CO.

Publication

1. Tadahiro Taniguchi, Shogo Nagasaka, Kentarou Hitomi, Naiwala P. Chandrasiri, and Takashi Bando,
Semiotic Prediction of Driving Behavior using Unsupervised Double Articulation Analyzer
2012 IEEE Intelligent Vehicles Symposium, 849 – 854 .(2012)  [PDF]
2. Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro Taniguchi, and Kentarou Hitomi,
Contextual Scene Segmentation of Driving Behavior based on Double Articulation Analyzer
IEEE/RSJ International Conference on Intelligent Robots and Systems 2012 (IROS 2012), 4847-4852 .(2012)
3. Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, and Tadahiro Taniguchi
Drive Video Summarization based on Double Articulation Structure of Driving Behavior
ACM Multimedia 2012, .(2012)

2. Interactive Automatic Song Writer using Nonparametric Bayesian Approach

Music composition is one of the human creative activities. However, song writing is still a difficult problem for most people without professional skills and knowledge about music. We developed an automatic song generation algorithm that enables users to interactively write a song on the basis of a machine learning technique, such as the Bayesian nonparametrics.

Publication

1. Akira Shirai and Tadahiro Taniguchi, A Proposal of an Interactive Music Composition System Using Gibbs Sampler, 14th International Conference on Human-Computer Interaction ( Human-Computer Interaction, Part I, HCII 2011, LNCS 6761), 490-497 .(2011)  [PDF]

2. Akira Shirai and Tadahiro Taniguchi, A Proposal of the Melody Generation Method using Variable-order Pitman-Yor Language Model,  Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.25 (6), pp. 901-913 .(2013) [PDF] (in Japanese)

Example:IWATE fall in love[youtube]

3. Recommendation Methods on Social Network Service

Social networks have a lot of information on their networks and in the documents that the users output. We can develop many kinds of services by mining the latent information from these social networks. We have developed a recommendation algorithm on a social network service.

Publication

1. Hiroyuki Koga and Tadahiro Taniguchi, Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics, 14th International Conference on Human-Computer Interaction ( Human-Computer Interaction, Part I, HCII 2011, LNCS 6761), 461-470. (2011)

2. Kenya Sudo, Shogo Nagasaka, Kuniaki Kobayashi, Tadahiro Taniguchi, and Toshiaki Takano, Encouraging User Interaction of Social Network Through Tweet Recommendation Using Community Structure, 2013 Conference on Technologies and Applications of Artificial Intelligence, 300-305 . (2013)