A proposal for an event-based representational formalism

We outline a formalism for structural, or symbolic, representation, the necessity of which has been acutely felt not just in artificial intelligence and pattern recognition, but also in the natural sciences, particularly biology. At the same time, biology has been gradually edging to the forefront of sciences, although the reasons obviously have nothing to do with its state of formalization or maturity. Rather, the reasons have to do with the growing realization that the objects of biology are not only more important (to society) and interesting (to science), but that they also more explicitly exhibit the evolving nature of all objects in the Universe. It is this view of objects as evolving structural entities/processes that we aim to formally address here, in contrast to the ubiquitous mathematical view of objects as points in some abstract space. In light of the above, the paper is addressed to a very broad group of scientists. One can gain an initial intuitive understanding of the proposed representation by generalizing the temporal process of the (Peano) construction of natural numbers: replace the single structureless unit out of which a number is built by multiple structural ones. An immediate and important consequence of the distinguishability (or multiplicity) of units in the construction process is that we can now see which unit was attached and when. Hence, the resulting (object) representation for the first time embodies temporal structural information in the form of a formative, or generative, object “history”

[1]  Georges Ifrah,et al.  The Universal History of Numbers , 1998 .

[2]  Lev Goldfarb,et al.  On the foundations of intelligent processes - I. An evolving model for pattern learning , 1990, Pattern Recognit..

[3]  Lewis Wolpert,et al.  Principles of Development , 1997 .

[4]  R. Feynman QED: The Strange Theory of Light and Matter , 1985 .

[5]  Sean B. Carroll,et al.  Endless forms most beautiful : the new science of evo devo and the making of the animal kingdom , 2005 .

[6]  M. Ridley Triumph of the embryo? , 1992, Nature.

[7]  Jean Alexandre Dieudonné,et al.  The Work of Nicholas Bourbaki , 1970 .

[8]  Oleg Golubitsky,et al.  What is a structural representation , 2001 .

[9]  Oleg Golubitsky,et al.  What is a structural representation ? Fourth variation ∗ , 2005 .

[10]  A. D. Ritchie The Dictionary of Philosophy , 1945, Nature.

[11]  Lev Goldfarb,et al.  What is distance and why do we need the metric model for pattern learning? , 1992, Pattern Recognit..

[12]  James Jeans,et al.  The new background of science , 1933 .

[13]  K. Ford The Quantum World: Quantum Physics for Everyone , 2004 .

[14]  Brian K. Hall,et al.  Keywords and Concepts in Evolutionary Developmental Biology , 2006 .

[15]  Lev Goldfarb,et al.  THE UNIFIED LEARNING PARADIGM: A FOUNDATION FOR AI , 2007 .

[16]  Lev Goldfarb,et al.  Why Classical Models for Pattern Recognition are Not Pattern Recognition Models , 1999 .

[17]  O. Golubitsky On the formalization of the evolving transformation system model , 2004 .

[18]  L Wolpert,et al.  Principles of Development, 2nd Edition , 2002 .

[19]  Sean Falconer On the Evolving Transformation System Representation of Fairy Tales , 2008 .

[20]  Oleg Golubitsky,et al.  On the generating process and the class typicality measure , 2002 .

[21]  Robin I. M. Dunbar The Trouble With Science , 1995 .

[22]  J. Stillwell,et al.  Symmetry , 2000, Am. Math. Mon..

[23]  Induction , 1999 .

[24]  M. Fowler Patterns , 2021, IEEE Softw..

[25]  Noam Chomsky Knowledge of language: its nature, origin, and use , 1988 .

[26]  L. Goldfarb,et al.  Representational formalisms : what they are and why we haven ’ t had any , 2006 .

[27]  Richard Schlegel,et al.  Time and the physical world , 1968 .

[28]  Edmund Landau,et al.  Foundations of analysis , 2001 .

[29]  Virendrakumar C. Bhavsar,et al.  Can a vector space based learning model discover inductive class generalization in a symbolic environment? , 1995, Pattern Recognit. Lett..

[30]  N. David Mermin,et al.  Boojums All The Way Through , 1990 .

[31]  Gutkin Alexander,et al.  Towards formal structural representation of spoken language : an evolving transformation system (ETS) approach , 2006 .

[32]  Noam Chomsky Knowledge of Language , 1986 .

[33]  Dmitry Korkin,et al.  A new model for molecular representation and classification: formal approach based on the ets framework , 2003 .

[34]  Lev Goldfarb,et al.  What is a symbolic measurement process? , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[35]  M. A. Aiserman REMARKS ON TWO PROBLEMS CONNECTED WITH PATTERN RECOGNITION , 1969 .

[36]  L. Wolpert Developmental Biology , 1968, Nature.

[37]  M. Laubichler Does EvoDevo equal regulatory evolution. Review of: Carroll, Sean B.: Endless forms most beautiful: the new science of Evo Devo and the making of the animal kingdom. New York [u.a.]: Norton 2005 , 2006 .

[38]  Horst Bunke,et al.  Hybrid methods in pattern recognition , 1987 .

[39]  L. Goldfarb,et al.  Ets learning of kernel languages , 2003 .

[40]  ETS representation of fairy tales , 2004 .

[41]  J. Losee A historical introduction to the philosophy of science , 1972 .

[42]  L. Goldfarb,et al.  Inductive learning with the evolving tree transformation system , 1996 .

[43]  Oleg Golubitsky,et al.  What is a Structural Representation? (Second Version) , 2004 .

[44]  Erwin Schrödinger,et al.  'Nature and the Greeks' and 'Science and Humanism' , 1996 .

[45]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[46]  S. Carroll Endless forms most beautiful : the new science of evo devo and the making of the animal kingdom , 2005 .

[47]  Gordon L. Kane The particle garden : our universe as understood by particle physicists , 1995 .

[48]  C. Morris,et al.  Philosophy of Science and Science of Philosophy , 1935, Philosophy of Science.

[49]  Nicholas Bourbaki,et al.  The Architecture of Mathematics , 1950 .

[50]  Stefan Wermter,et al.  A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning , 1998, Hybrid Neural Systems.

[51]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .