A Cognitive Approach for Optimal Minimization of State Transitions in Nondeterministic Finite Automata

Power set construction and its variants to eliminate "dead", "unreachable" states have been the prevalent methods of minimization from Non-Deterministic Finite Automata to Deterministic Finite Automata. The drawbacks for these were the lack of adaptive improvements into the minimization process. The adaptation by means of cognitive models - perception, reasoning and decision-making - is being proposed as a better solution while handling real life problems involving decision making on transitions of states. The non-determinism added by the ambiguity in parallel transition options are resolved by applying cognitive processes. The ACT-R architecture is found to be convenient for modeling systems whose functionality can be deconstructed into a set of states, with the transition functions being modeled as production rules. The resultant model reduces the number of states from 2k to manageable values.