Data-Driven Generation of Low-Complexity Control Programs
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Abstract
In this paper we study the problem of generating control programs, i.e.
strings of symbolic descriptions of control-interrupt pairs (or modes) from
input-output data. In particular, we take the point of view that such control
programs have an information theoretic content and thus that they can be more
or less effectively coded. As a result, we focus our attention on the problem
of producing low-complexity programs by recovering the shortest mode strings
as well as the strings that contain the smallest number of distinct modes. An
example is provided where the data is obtained by tracking ten roaming ants
in a tank.
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2004
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