Special Sessions

Special Sessionss
SS01
Robotic and Intelligent Systems

Ching-Chih Tsai (National Chung Hsing University, Taiwan)
Shun-Feng Su (National Taiwan University of Science and Technology, Taiwan)

In this special session, we intend to collect papers that reflect current progress in the Robotic and Intelligent Systems. We hope those papers can set a milestone for the Robotic and Intelligent Systems and also provide ideas for further exploration in this promising research area. At present, there are five papers in this topic in our collected papers.

SS02
Intelligent Systems and Fuzzy Modeling

Jin-Tsong Jeng (National Formosa University, Taiwan)
Chen-Chia Chuang (National iLan University, Taiwan)

In this special session, we intend to collect papers that reflect current progress in the Intelligent Systems and Fuzzy Modeling. We hope those papers can set a milestone for the Intelligent Systems and Fuzzy Modeling and also provide ideas for further exploration in this promising research area. At present, there are five papers in this topic in our collected papers.

SS03
Interval Uncertainty

Martine Ceberio (University of Texas at El Paso, USA)
Vladik Kreinovich (University of Texas at El Paso, USA)

Interval uncertainty is closely related to fuzzy techniques: indeed, if we want to know how the fuzzy uncertainty of the inputs propagates through the data processing algorithm, then the usual Zadeh’s extension principle is equivalent to processing alpha-cuts (intervals) for each level alpha.
This relation between intervals and fuzzy computations is well known, but often, fuzzy researchers are unaware of the latest most efficient interval techniques and thus use outdated less efficient methods. One of the objectives of the proposed session is to help fuzzy community by explaining the latest interval techniques and to help interval community to better understand the related interval computation problems.
Yet another relation between interval and fuzzy techniques is that the traditional fuzzy techniques implicitly assume that experts can describe their degree of certainty in different statements by an exact number. In reality, it is more reasonable to expect experts to provide only a rage (interval) of possible values — leading to interval-valued fuzzy techniques that, in effect, combine both types of uncertainty.

SS04
Forward Looking Decision-making under Uncertainty

Mikael Collan (LUT University, Finland)
Pasi Luukka (LUT University, Finland)

The session concentrates on supporting forward looking decision-making in the presence of uncertainty. The session is not “method specific”. The decision-making support context is not and may include for example economic, health- and engineering related decision-making.