Discovery is the process to find out or bring to light of that which was previously unknown. Prediction is the action to make some future event known in advance, especially on the basis of special knowledge, and therefore, it is a notion must relate to a point of time to be considered as the reference time. For any discovery and/or prediction, both the discovered and/or predicted thing and its truth must be unknown before the completion of discovery and/or prediction process. Because reasoning is the only way to draw previously unknown new conclusions from given premises, there is no discovery and/or prediction process that does not invoke reasoning. Relevant reasoning requires that in a valid reasoning process, for any argument to be valid there must be some connection of meaning, i.e., some relevance, between its premises and its conclusion, among other things. Because any discovery or prediction has not an explicitly given target as its goal, relevant reasoning must play the key role in discovery and/or prediction.
This Invited Session provides a forum for presentations and discussions from researchers and/or practitioners who are interested in those issues involved with applying relevant reasoning to finding or predicting new facts, concepts, and principles from known facts or assumed hypotheses, e.g., anticipation, automated theorem finding, autonomous evolution, epistemic process, inference rule generation, knowledge discovery, knowledge acquisition, theory formation, and so on.
Topics of interest include but are not limited to:
Papers are invited from prospective authors with interests in the indicated session topics and related areas of application. All contributions must be original and high quality, should not have been published elsewhere and should not be intended for publication elsewhere during the review period or time of the conference. Submitted papers will be thoroughly reviewed by members of the Invited Session Program Committee for originality, significance, and relevance, and may be accepted for oral or poster presentation. The conference proceedings will be published by Springer-Verlag in Lecture Notes in AI as part of the LNCS/LNAI series. Extended versions of selected papers will be considered for publication in the KES Journal (International Journal of Knowledge-Based and Intelligent Engineering Systems) published by IOS Press.
Submitted papers should strictly follow the instructions to LNCS authors (http://www.springer.com/east/home/computer/lncs?SGWID=5-164-2-72376-0&SHORTCUT=www.springer.com/sgw/cda/frontpage/0,11855,5-164-2-72376-0,00.html). Please note that the required paper length is 8 pages in Springer format. Papers longer than this will be subject to a penalty charge. Papers much longer or shorter than the required length may be directly rejected without review. All accepted papers must be presented by one of the authors, who must register the conference. Authors will be limited to one paper per registration.
Papers must be submitted in PDF format using the PROSE (http://www.prosemanager.com/) system, and selecting the Invited Session IS48 "Relevant Reasoning for Discovery and Prediction".
Jingde Cheng, Dr.
Professor of Computer Science
Department of Information and Computer Sciences
Saitama University
255 Shimo-Okubo, Sakura-Ku, Saitama, 338-8570, Japan
E-mail: cheng@ics.saitama-u.ac.jp
URL: http://www.aise.ics.saitama-u.ac.jp/~cheng/index.html
Yuichi Goto, Dr.
Assistant Professor of Computer Science
Department of Information and Computer Sciences
Saitama University
255 Shimo-Okubo, Sakura-Ku, Saitama, 338-8570, Japan
E-mail: gotoh@aise.ics.saitama-u.ac.jp
URL: http://www.aise.ics.saitama-u.ac.jp/~gotoh/index.html
Dr. Jingde Cheng
Dr. Ying Gao
Dr. Yuichi Goto
Dr. Shisuke Nara
Dr. Takahiro Tagawa
Dr. Noriaki Yoshiura