Submissions should be 4-6 pages in length and must follow the NIPS 2011 format (http://nips.cc/PaperInformation/StyleFiles).  Submissions need not to be anonymous and should include the title, authors' names, postal and email addresses, and an abstract not to exceed 150 words.

The workshop will include discussed talks and poster sessions. We invite submissions of papers describing the technical content of proposed contribution. The selected submissions may be accepted either as an oral presentation or as a poster presentation. We especially encourage submissions that directly address any of the topics, but related papers are also welcomed.

The workshop’s targeted audience is:

Distributed dynamic decision-making and learning under uncertainty in complex and changing situations are emerging as the key competencies required to support future information-based systems. The Bayesian paradigm is acknowledged to provide a consistent and rigorous theoretical basis for joint learning and dynamic decision-making. The established theory already provides a class of efficient adaptive strategies. However, this approach fails to overcome the computational complexity barrier encountered in complex settings.

Quality of maintaining of complex man-machine systems very much depends on experience, skills and performance of the human decision-makers (operators) managing the system. The task is complicated by complexity and dimensionality of the system managed as well as limited abilities of the operator.

Textbook: