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[[Media:Agenda.pdf|Agenda PDF]]
== Abstracts ==
 
 
 
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'''To a Mathematical Theory of Evolution and Biological Creativity'''
 
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Chaitin, Gregory (gjchaitin@gmail.com)
 
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IBM Watson Research Center
 
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We present an information-theoretic analysis of Darwin’s theory of evolution, modeled as a hill-climbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur and to characterize the rate of evolutionary progress, i.e., the rate of biological creativity.
 
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Links: [[File:Darwin.pdf]]
 
 
 
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'''The Vocabulary of Grammar-Based Codes and the Logical Consistency of Texts'''
 
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Debowski, Lukasz (ldebowsk@ipipan.waw.pl)<br>
 
Polish Academy of Sciences<br>
 
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We will present a new explanation for the distribution of words in
 
natural language which is grounded in information theory and inspired
 
by recent research in excess entropy. Namely, we will demonstrate a
 
theorem with the following informal statement: If a text of length <math>n</math>
 
describes <math>n^\beta</math> independent facts in a repetitive way then the
 
text contains at least <math>n^\beta/\log n</math> different words.  In the
 
formal statement, two modeling postulates are adopted. Firstly, the
 
words are understood as nonterminal symbols of the shortest
 
grammar-based encoding of the text. Secondly, the text is assumed to
 
be emitted by a finite-energy strongly nonergodic source whereas the
 
facts are binary IID variables predictable in a shift-invariant
 
way. Besides the theorem, we will exhibit a few stochastic processes
 
to which this and similar statements can be related.
 
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Links: [[http://arxiv.org/abs/0810.3125]] and [[http://arxiv.org/abs/0911.5318]]
 
 
 
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'''Automatic Identification of Information-Processing Structures in Cellular Automata'''
 
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Mitchell, Melanie (mm@cs.pdx.edu)
 
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SFI & Portland State University
 
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Cellular automata have been widely used as idealized models of natural spatially-extended dynamical systems.  An open question is how to best understand such systems in terms of their information-processing capabilities.   In this talk we address this question by describing several approaches to automatically identifying the structures underlying information processing in cellular automata. In particular, we review the computational mechanics methods of Crutchfield et al.,  the local sensitivity and local statistical complexity filters proposed by Shalizi et al., and the information theoretic filters proposed by Lizier et al.   We illustrate these methods by applying them to several one- and two-dimensional cellular automata that have been designed to perform the so-called density (or majority) classification task.
 
 
 
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Latest revision as of 01:17, 3 January 2011