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Abstract
Inductive class representation and the more comprehensive evolving transformation system (ETS) are congenial to the subject matter of anticipation. In substantiating this assertion, we examine the epistemological premises of a new form of representation, of interest to pattern recognition and Artificial Intelligence (AI), but even more to the study of living systems. Some concepts, such as classes, time and time scale, and generative processes are examined in detail with respect to their pertinence to anticipation. Finally, pattern generation and ETS programming are suggested.
Acknowledgements
This work would not have been possible without the cooperation of all members of antÉ – Institute for Research in Anticipatory Systems. Let me acknowledge the contribution of Dr Navzer Engineer (2005-2008), Cassandra Emswiler, Dr Gaurav Prahdan, Nathan Felde, and Dr Jeffrey V. Nickerson. Over the years, Dr B. Prabhakaran and Dr Sandra Chapman have provided valuable input. Funding for this work was provided by the University of Texas at Dallas, antÉ – Institute for Research in Anticipatory Systems, and the Deutsche Forschungs Gemeinschaft (DFG). Leibniz's memorial coin is reproduced with the kind permission of the Gottfried Wilhelm Leibniz Bibliothek (Hannover, Germany).