Data-Driven Generation of Low-Complexity Control Programs
Florent Delmotte, Magnus Egerstedt, Adam Austin · 2004
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. 1