Abstract
Several general search techniques such as genetic programming and simulated annealing have recently been investigated for synthesising programs from specifications of desired objective behaviours. In this context, these techniques explore the space of all candidate programs by performing local changes to candidates selected by means of a measure of their fitness w.r.t the desired objectives. Previous performance results advocated the use of simulated annealing over genetic programming for such problems. In this paper, we investigate the application of these techniques for the computation of deterministic strategies solving symbolic Discrete Controller Synthesis (DCS) problems, where a model of the system to control is given along with desired objective behaviours. We experimentally confirm that relative performance results are similar to program synthesis, and give a complexity analysis of our simulated annealing algorithm for symbolic DCS.
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