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RSCD 2005 |
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Call for Papers | ||||||||
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Rough set theory, proposed by Z. Pawlak in 1982, is a model of approximate reasoning. The main idea is based on the indiscernibility relation that describes indistinguishable objects. Concepts are represented by lower and upper approximations, which can be viewed as uncertainty management. Although the fundamentals of rough sets is mathematical, they covers a broad spectrum of application areas, such as biomedical informatics, financing, music an so on. On the other hand, Chance Discovery focuses on the ¡°chance¡±, a rare event or situation., which provides a deep impact on human decision making or problem solving. Since it rarely happens, computers cannot deal with it easily. Chance Discovery researchers focus on the facts that human knowledge and computer¡¯s capability are complementary for the detection of a chance: they view chance discovery as intercommunications between domain experts and computers. Rough Sets and Chance Discovery have different views about uncertainty management. While rough sets focus on the information granularity and approximation of knowledge from the viewpoint of mathematics, chance discovery stresses on the role of human experts in dealing with uncertainty. Rough Sets: theoretical, Chance Discovery: empirical. This time researchers of both areas are put together into one workshop, discuss all the issues on uncertainty management with data. The workshop gives a special opportunity for researchers on rough sets and chance discovery, which exchange their ideas.
In a number of different areas, researchers in Business Informatics and Discovery Science recently became interested in events or situations that affect human decision making in that they are viewed as opportunities or risks. This interest has resulted in a new and highly active area, referred to as Chance Discovery. A chance is such a rare event or a situation, which provides opportunities or risks for human decision making or problem solving. Noticing such an event is described as discovery of a chance, not by chance. Therefore Chance Discovery can be characterized in terms of becoming aware of a chance and explaining its significance. The development of chance discovery methods lead to a variety of applications in areas such as management, marketing, medical diagnosis, earthquake forecast, and product developments. In these applications, the discovery of a chance relied on various sub-areas of artificial intelligence, such as data mining, fuzzy theories, logical abductive reasoning, image understanding, computer-supported collaborative work, and others. However, we also should emphasize successful cases of chance discovery were always carried out with interactions between computer and human, both facing the real world environment. For this reason, Chance Discovery in turn has been influenced by business sciences, marketing science, social psychology, linguistics, CSCW, and others. Reflecting this interdisciplinary growth of Chance Discovery, we organize a new workshop on contributions to all aspects of the chance-discovery process, i.e., scenario discovery/creation from data, human-machine interaction, human-human communication, and also human interaction with any part of environment.
All papers must be in Word or PDF format and may not exceed 4 pages in length, including figures, tables and references. Any papers exceeding 4 pages will be charged a $50 fee per additional page. Papers should be formatted with double columns and should be single-spaced in a 10 point font such as Times Roman. See the paper template in JCIS submission instructions (http://www.jcis.org/pages/call4paper.aspx). All papers MUST follow this format.
Please submit your papers by April 8, 2005.
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