Computational study demonstrates a modified Nerode technique to synthesize finite-state machines from sample behaviors, indicating a tunable trade-off between machine complexity and accuracy.
The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizing machines from finite subsets of their input-output behavior. The synthesis procedure includes a parameter that one may adjust to obtain machines that represent the desired behavior with varying degrees of accuracy and that consequently have varying complexities. We discuss some of the uses of the method, including an application to a sequential learning problem.
No takes yet. Share an insight, caveat, or question.
Biermann et al. (1972) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: