Notice of Violation of IEEE Publication Principles: Coordinating eye-hand action via partially-observable Markov decision processes

Notice of Violation of IEEE Publication Principles

"Coordinating Eye-hand Action via Partially-observable Markov Decision Processes"
by Yanyun Cheng, Songhao Zhu, Zhiwei Liang, Lili Fan
in the Proceedings of the 31st Chinese Control Conference (CCC), 2012, pp. 3969-3973

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"A POMDP Model of Eye-Hand Coordination"
by Tom Erez, Julian J. Tramper, William D. Smart, Stan C. A. M. Gielen
in the Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence and the Twenty-Third Innovative Applications of Artificial Intelligence Conference, 2011, pp. 952-957


This paper presents a optimization model of eye-hand coordination, which is based on a partially-observable markov decision processes with 17 continuous state dimensions. the maximum likelihood observation is always obtained. Maximum likelihood observation can be obtained by the maximum likelihood observation. Since the globally-optimal solution for a high-dimensional domain is computationally intractable, local optimization in the belief domain is adopted to model eye-hand coordination, where the eyes' saccades disambiguate the scene in a task-relevant manner, and the hands' motions anticipate the eyes' saccades. Finally, the model is validated through a behavioral experiment.