Computing with Finite Automata: The Unknown Component Problem Solving Flow

1. Computing with Finite Automata 290N: The Unknown Component Problem Lecture 9

2. Outline • Problem solving flow • Example of a traffic light controller • Representation of automata • Simple operations • Complementing (“complement”) • Completing (“complete”) • Filtering states • Making prefix closed (“prefix”) • Progressive (“progressive”) • Moore-reduction (“moore”) • State minimization (“minimize”) • Reachability analysis • Product computation (“product”) • Verification by language containment (“check”) • Determinization by subset construction (“determinize”) • Don’t-care minimization (“dcmin”)

3. Problem Solving Flow • Determine the interaction topology • Specify the fixed part and the spec as automata, FSMs, or multi-level multi-valued networks • Create the script, which implements the flow • Special attention should be paid to projection and lifting of variables (command “support”) because it is closely related to the selected topology • Run the script on MVSIS and debug it if necessary • Analyze the resulting solution • Is it prefix closed and progressive? (commands “prefix”, “progressive”) • Is it deterministic as an FSM? (command “check_nd”) • Is it state-minimum? (command “minimize”) • Compare the language of this solution with other solutions if available (command “volume”) • Make conclusions • Formulate and prove new theorems • Create new examples

4. Example: Traffic Light Controller General Topology This example Specification Specification S S I O z Fixed Fixed F F V U v Unknown Unknown X X

5. z Fixed F v Traffic Light Controller (fixed part) .model fixed .inputs v z .outputs Acc .mv v 2 wait go .mv z 3 red green yellow .mv CS, NS 3 Fr Fg Fy .latch NS CS .reset CS Fr .table ->Acc 1 .table v z CS ->NS wait red Fr Fr go red Fr Fg wait green Fg Fg go green Fg Fy wait yellow Fy Fy go yellow Fy Fr .end z = {red, green, yellow} v = {wait, go}

6. Specification S z Traffic Light Controller (spec) .model spec .inputs z .outputs Acc .mv z 3 red green yellow .mv CS,NS 4 S1 S2 S3 S4 .table ->Acc 1 .latch NS CS .reset CS S1 .table z CS ->NS red S1 S2 red S2 S3 green S3 S4 yellow S4 S1 .end

7. Specification S z Fixed F v Unknown X Traffic Light Controller (script) echo "Synthesis ..." determinize -lci spec.mva spec_dci.mva support v(2),z(3) spec_dci.mva spec_dci_supp.mva support v(2),z(3) fixed.mva fixed_supp.mva product -l fixed_supp.mva spec_dci_supp.mva p.mva supportv(2) p.mva p_supp.mva determinize -lci p_supp.mva p_dci.mva progressive-i 0 p_dci.mva x.mva echo "Verification ..." support v(2),z(3) x.mva x_supp.mva product x_supp.mva fixed_supp.mva prod.mva support v(2),z(3) spec.mva spec_supp.mva check prod.mva spec_supp.mva

8. v Unknown X Traffic Light Controller (solution) .model solution .inputs v .outputs Acc .mv v 2 wait go .mv CS, NS 4 \ FrS1 FrS2 FgS3 FyS4 .latch NS CS .reset CS FrS1 .table ->Acc 1 .table v CS ->NS wait FrS1 FrS2 go FrS2 FgS3 go FgS3 FyS4 go FyS4 FrS1 .end

9. Specification S z Fixed F v Unknown X Traffic Light Controller (script2) echo "Synthesis ..." determinize -lci spec.mva spec_dci.mva support v(2),z(3) spec_dci.mva spec_dci_supp.mva support v(2),z(3) fixed.mva fixed_supp.mva product -l fixed_supp.mva spec_dci_supp.mva p.mva supportz(3),v(2) p.mva p_supp.mva determinize -lci p_supp.mva p_dci.mva progressive-i 1 p_dci.mva x.mva echo "Verification ..." support v(2),z(3) x.mva x_supp.mva product x_supp.mva fixed_supp.mva prod.mva support v(2),z(3) spec.mva spec_supp.mva check prod.mva spec_supp.mva

10. Further Experiments • Run both scripts and see how solution differs • Make Spec non-deterministic and see what happens • Make Fixed non-deterministic and see what happens • Try more complex Fixed • For example, make Fixed depend on an additional variable s, which shifts it from one set of states to another set of states • In one set, Fixed behaves as before • In other set, Fixed already behaves according to the spec

11. An Extension of Fixed Old states New states

12. Specification Specification S S z z Fixed Fixed F F v v Unknown Unknown X X Two Topologies to Try s s

13. Outline • Problem solving flow • Example of a traffic light controller • Representation of automata • Simple operations • Complementing (“complement”) • Completing (“complete”) • Filtering states • Making prefix closed (“prefix”) • Progressive (“progressive”) • Moore-reduction (“moore”) • State minimization (“minimize”) • Reachability analysis • Product computation (“product”) • Verification by language containment (“check”) • Determinization by subset construction (“determinize”) • Don’t-care minimization (“dcmin”)

14. Representation of Automata • Completely explicit (MVSIS package “au”) • Both STG and transition conditions are represented explicitly (STG is a graph; conditions are SOPs) • Completely implicit (monolithic) (MVSIS package “lang”) • Automaton is represented by a single transition relation and char functions of accepting states • Completely implicit (partitioned) (MVSIS package “mvn” (mvnSolve.c)) • Can only be used for automata derived from multi-level networks • Automaton is represented by a set of partitions (one partition for each latch excitation function) • Hybrid representation (MVSIS package “aut”) • STG is represented explicitly (as a graph) • Transition conditions are represented implicitly as BDDs

15. Simple Operations • Complementing • swap the sets of accepting and non-accepting states • deterministic automata only! • Completing • For each state, compute the input domain when the transitions are defined • If this domain is constant 1 for all states, the automaton is complete • Otherwise • create a new non-accepting state (DC state) with the self-loop under all inputs • create transitions from each incompletely specified state into the DC state, under the previously undefined condition

16. Filtering States • Prefix-closed • Removes all the non-accepting states • Removes the accepting states not reachable from the initial state • Progressive (I-progressive) • Iteratively removes all the states whose I/O behavior represented as a multi-output relation is not well-defined • Moore-reduction • Given an arbitrary Mealy machine, reduce it to a Moore machine

17. Example of Prefixed Close

18. Example of Progressive

19. Example of Moore-Reduction CSF computed after splitting latches of benchmark “dk27.blif” The results of Moore-reduction Number of inputs = 3.

20. Outline • Problem solving flow • Example of a traffic light controller • Representation of automata • Simple operations • Complementing (“complement”) • Completing (“complete”) • Filtering states • Making prefix closed (“prefix”) • Progressive (“progressive”) • Moore-reduction (“moore”) • State minimization (“minimize”) • Reachability analysis • Product computation (“product”) • Verification by language containment (“check”) • Determinization by subset construction (“determinize”) • Don’t-care minimization (“dcmin”)

21. State Minimization of FA • Requirements for the automaton • Deterministic (if not, first determinize) • Complete (if not, first complete) • Definition of state equivalence • Two ways of computing equivalence classes • Implicit • Explicit • The explicit algorithm in detail • Example

22. State Equivalence of FA • Definition. A string is accepted by the automation in state s iff it drives the automaton into an accepting state. • Definition. Two states s1 and s2 are distinguishable iff there exists a string, which is accepted in state s1 and not accepted in state s2. • Definition. States s1 and s2 are equivalent if they are not distinguishable. Example: States A and C are distinguishable States B and C are equivalent

23. Outline of the Algorithm • The automaton is given by • State transition graph • The set of accepting states • Compute the set of distance-0 distinguishable pairs by combining each accepting state with each non-accepting state • For each pair, find all the pairs reachable in backward traversal from the distinguishable pairs, under all input combinations • Collect these pairs and explore them until no new pairs can be found • The remaining pairs are pairs of equivalent states • Reduce the automaton by replacing each state by one selected representative of its equivalence class

24. Implicit Implementation • The automaton is given by • Transition relation R(x,cs,ns) • Characteristic function of accepting states A(cs) • Compute the set of distance-0 distinguishable pairs (when one state is accepting while the other is not) • D0(cs,cs’) = A(cs)  A(cs’) • Compute the pair transition relation • P(cs,cs’,ns,ns’) = x [R(x,cs,ns) & R(x,cs’,ns’)] • Starting from the distance-0 distinguishable pairs, iteratively compute distance-k distinguishable pairs, until convergence • Di+1(cs,cs’) = ns,ns’ [P(cs,cs’,ns,ns’) & Di(ns,ns’)] • The equivalence relation is • E(cs,cs’) = NOT[Di+1(cs,cs’)] • Reduce the automaton by replacing each state by one representative taken from its equivalence class • P(cs,cs’) = CompatibleProjection( E(cs,cs’), {cs} ) • R(x,cs,ns) = cs’,ns’[ R(x,cs’,ns’) & P(cs,cs’) & P(ns,ns’) ]

25. Explicit Implementation • The automaton • The linked list of states • The accepting states are marked • Additional data structures • Q: The FIFO queue of distinguishable state pairs to be explored • H: The hash table hashing every pair into {visited, not visited} • Initialization for each accepting state s for each non-accepting state s’ insert pair (s,s’) into Q and H • Computation while Q is not empty, extract one pair (s,s’) from Q for each pair (t,t’), which transits into (s,s’) under some input if (t,t’) is not in H (that is, (t,t’) has not been visited) insert pair (t,t’) into Q and into H

26. Reducing the Automaton • The automaton • The linked list of states • The accepting states are marked • The equivalence relation • Maps pair (s,s’) into {distinguishable, equivalent} • The same as hash table H: visited = distinguishable; not visited = equivalent • Computation • Construct the equivalence classes of states using the equivalence relation • Select one representative state from each equivalence class • Create the mappingof each state in the original automaton into the representative state from its equivalence class start the new automaton add a new state for each representative state of the old automaton for all representative states s1 for each transition (s1->s2) from the representative state s1 into some other state s2 add transition from the new state corresponding to s1 into the new state corresponding to s2 • Set the new initial state to be the new state corresponding to the representative of the class, to which the original initial state belongs

27. Example of State Minimization • Distinguishable pairs after initialization • (A,DC), (C,DC), (B,DC) • Computed distinguishable pairs • (A,DC)  (A,C) • (A,DC)  (A,B) • Remaining equivalent pairs • (B,C) • The derived reduced graph