A dynamic model for the state assignment problem

Traditionally, state assignment algorithms follow the two-step strategy of first constraint generation and secondly constraint-guided encoding. There are well known drawbacks in both currently used models for constraint generation. Approaches following the input model generate face constraints without taking into account the sharing of logic among next state lines. Approaches following the input-output model generate face constraints for a priori determined set of dominance/disjunctive relations among the codes of the states which may not hold in final encoding. To overcome these limitations, we propose a dynamic input model which implements both above cited steps concurrently. The dynamic constraints are of the face type but they are generated during the encoding process and s