TY - GEN

T1 - Minimization of finite state automata through partition aggregation

AU - Björklund, J.

AU - Cleophas, L.G.W.A.

PY - 2017

Y1 - 2017

N2 - We present a minimization algorithm for finite state automata that finds and merges bisimulation-equivalent states, identified through partition aggregation. We show the algorithm to be correct and run in time O(n 2 d 2 |Σ|), where n is the number of states of the input automaton M, d is the maximal outdegree in the transition graph for any combination of state and input symbol, and |Σ| is the size of the input alphabet. The algorithm is slower than those based on partition refinement, but has the advantage that intermediate solutions are also language equivalent to M. As a result, the algorithm can be interrupted or put on hold as needed, and the derived automaton is still useful. Furthermore, the algorithm essentially searches for the maximal model of a characteristic formula for M, so many of the optimisation techniques used to gain efficiency in SAT solvers are likely to apply.

AB - We present a minimization algorithm for finite state automata that finds and merges bisimulation-equivalent states, identified through partition aggregation. We show the algorithm to be correct and run in time O(n 2 d 2 |Σ|), where n is the number of states of the input automaton M, d is the maximal outdegree in the transition graph for any combination of state and input symbol, and |Σ| is the size of the input alphabet. The algorithm is slower than those based on partition refinement, but has the advantage that intermediate solutions are also language equivalent to M. As a result, the algorithm can be interrupted or put on hold as needed, and the derived automaton is still useful. Furthermore, the algorithm essentially searches for the maximal model of a characteristic formula for M, so many of the optimisation techniques used to gain efficiency in SAT solvers are likely to apply.

UR - http://www.scopus.com/inward/record.url?scp=85013436481&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-53733-7_16

DO - 10.1007/978-3-319-53733-7_16

M3 - Conference contribution

SN - 978-3-319-53732-0

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 223

EP - 235

BT - Language and Automata Theory and Applications

A2 - Drewes, Frank

A2 - Martín-Vide, Carlos

A2 - Truthe, Bianca

PB - Springer

CY - Dordrecht

ER -