First(ish) version of the UIO solver

README.md

@@ -1,2 +1,30 @@
-# satuio
-Using SAT solvers to construct UIOs and ADSs
+satuio
+======
+
+Using SAT solvers to construct UIOs and ADSs for Mealy machines.
+
+
+## Dependencies
+
+This project uses Python. It uses the following packages which can be
+installed with `pip`.
+
+* pysat
+* tqdm
+
+
+## Usage
+
+(Note: this project is still WIP and the commands will change.)
+
+```bash
+#              <file>        <length>
+python3 uio.py examples/esm-0.dot 3
+```
+
+
+## Copyright
+
+© Joshua Moerman, Open Universiteit
+
+

examples/quic.dot

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+digraph g {
+__start0 [label="" shape="none"];
+
+	s0 [shape="circle" label="0"];
+	s1 [shape="circle" label="1"];
+	s2 [shape="circle" label="2"];
+	s3 [shape="circle" label="3"];
+	s4 [shape="circle" label="4"];
+	s5 [shape="circle" label="5"];
+	s6 [shape="circle" label="6"];
+	s0 -> s1 [label="INIT-CHLO / REJ"];
+	s0 -> s0 [label="GET / PRST"];
+	s0 -> s0 [label="CLOSE / closed"];
+	s0 -> s0 [label="FULL-CHLO / PRST"];
+	s0 -> s1 [label="0RTT-CHLO / REJ"];
+	s1 -> s2 [label="INIT-CHLO / REJ"];
+	s1 -> s3 [label="GET / EXP"];
+	s1 -> s1 [label="CLOSE / closed"];
+	s1 -> s4 [label="FULL-CHLO / shlo"];
+	s1 -> s4 [label="0RTT-CHLO / shlo"];
+	s2 -> s2 [label="INIT-CHLO / REJ"];
+	s2 -> s2 [label="GET / EXP"];
+	s2 -> s2 [label="CLOSE / closed"];
+	s2 -> s2 [label="FULL-CHLO / EXP"];
+	s2 -> s4 [label="0RTT-CHLO / shlo"];
+	s3 -> s1 [label="INIT-CHLO / REJ"];
+	s3 -> s5 [label="GET / EXP"];
+	s3 -> s2 [label="CLOSE / closed"];
+	s3 -> s2 [label="FULL-CHLO / EXP"];
+	s3 -> s4 [label="0RTT-CHLO / shlo"];
+	s4 -> s1 [label="INIT-CHLO / REJ"];
+	s4 -> s2 [label="GET / http"];
+	s4 -> s6 [label="CLOSE / closed"];
+	s4 -> s2 [label="FULL-CHLO / EXP"];
+	s4 -> s4 [label="0RTT-CHLO / shlo"];
+	s5 -> s1 [label="INIT-CHLO / REJ"];
+	s5 -> s2 [label="GET / EXP"];
+	s5 -> s2 [label="CLOSE / closed"];
+	s5 -> s2 [label="FULL-CHLO / EXP"];
+	s5 -> s4 [label="0RTT-CHLO / shlo"];
+	s6 -> s1 [label="INIT-CHLO / REJ"];
+	s6 -> s6 [label="GET / PRST"];
+	s6 -> s6 [label="CLOSE / closed"];
+	s6 -> s6 [label="FULL-CHLO / PRST"];
+	s6 -> s4 [label="0RTT-CHLO / shlo"];
+
+__start0 -> s0;
+}

uio.py

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+from pysat.solvers import Solver
+from pysat.formula import IDPool
+from pysat.card import CardEnc, EncType
+import time
+import argparse
+from tqdm import tqdm
+
+solver_name = 'g3'
+verbose = True
+
+start = time.time()
+def measure_time(*str):
+  global start
+  now = time.time()
+  print('***', *str, "in %.3f seconds" % (now - start))
+  start = now
+
+
+# Automaton
+
+parser = argparse.ArgumentParser()
+parser.add_argument('filename', help='File of the mealy machine (dot format)')
+parser.add_argument('length', help="Length of the uio", type=int)
+args = parser.parse_args()
+
+length = args.length
+
+alphabet = set()
+outputs = set()
+states = set()
+bases = set(['s0', 's4', 's37', 's555'])
+
+delta = {}
+labda = {}
+
+# Quick and dirty .dot parser
+with open(args.filename) as file:
+  for line in file.readlines():
+    asdf = line.split()
+    if len(asdf) > 3 and asdf[1] == '->':
+      s = asdf[0]
+      t = asdf[2]
+      rest = ''.join(asdf[3:])
+      label = rest.split('"')[1]
+      [i, o] = label.split('/')
+
+      states.add(s)
+      states.add(t)
+      alphabet.add(i)
+      outputs.add(o)
+
+      delta[(s, i)] = t
+      labda[(s, i)] = o
+
+measure_time('Constructed automaton with', len(states), 'states and', len(alphabet), 'symbols')
+
+
+# Solver setup
+
+vpool = IDPool()
+solver = Solver(name=solver_name)
+
+# mapping van variabeles: x_... ->  x_i
+def var(x):
+  return(vpool.id(('uio', x)))
+
+# On place i we have symbol a
+def avar(i, a):
+  return var(('a', i, a))
+
+# Each state has its own path
+# On path s, on place i, there is output o
+def ovar(s, i, o):
+  return var(('o', s, i, o))
+
+# On path s, we are in state t on place i
+def svar(s, i, t):
+  return var(('s', s, i, t))
+
+# Extra variable (a la Tseytin transformation)
+# On path s, there is a difference on place i
+def evar(s, i):
+  return var(('e', s, i))
+
+# In order to re-use parts of the formula, we introduce
+# enabling variables. These indicate the fixed state for which
+# we want to compute an UIO. By changing these variables only, we
+# can keep most of the formula the same and incrementally solve it.
+# The fixed state is called the "base".
+def bvar(s):
+  return var(('base', s))
+
+# maakt de constraint dat precies een van de literals waar moet zijn
+def unique(lits):
+  # deze werken goed: pairwise, seqcounter, bitwise, mtotalizer, kmtotalizer
+  # anderen geven groter aantal oplossingen
+  # alles behalve pairwise introduceert meer variabelen
+  cnf = CardEnc.equals(lits, 1, vpool=vpool, encoding=EncType.seqcounter)
+  solver.append_formula(cnf.clauses)
+
+measure_time('Setup solver')
+
+
+# Construction
+
+# Guessing the word:
+# variable x_('in', i, a) says: on place i there is an input a
+for i in range(length):
+  unique([avar(i, a) for a in alphabet])
+
+# We should only enable one base state (this allows for an optimisation later)
+unique([bvar(base) for base in bases])
+
+for s in tqdm(states, desc="simple stuff"):
+  for i in range(length):
+    # variable x_('out', s, i, a) says: on place i there is an output o of the path s
+    unique([ovar(s, i, o) for o in outputs])
+
+    if i == 0:
+      # The paths start in the corresponding state
+      # This could be used to reduce some variables, but I'm lazy now
+      solver.add_clause([svar(s, 0, s)])
+    else:
+      # variable x_('state', s, i, t) denotes the path through the automaton starting in s
+      unique([svar(s, i, t) for t in states])
+
+# The path is consistent with the delta function
+# The outputs correspond to the output along the path
+# I have merged these loops, it was slightly faster
+for s in tqdm(states, desc="paths & outputs"):
+  for i in range(length):
+    for t in states:
+      sv = svar(s, i, t)
+      for a in alphabet:
+        av = avar(i, a)
+        # We couple i with i+1, and so skip the last iteration
+        if i < length-1:
+          # x_('s', s, i, t) /\ x_('in', i, a) => x_('s', s, i+1, delta(t, a))
+          # == -x_('s', s, i, t) \/ -x_('in', i, a) \/ x_('s', s, i+1, delta(t, a))
+          next_t = delta[(t, a)]
+          solver.add_clause([-sv, -av, svar(s, i+1, next_t)])
+        
+        # x_('s', state, i, t) /\ x_('in', i, a) => x_('o', i, labda(t, a))
+        # == -x_('s', state, i, t) \/ -x_('in', i, a) \/ x_('o', i, labda(t, a))
+        output = labda[(t, a)]
+        solver.add_clause([-sv, -av, ovar(s, i, output)])
+
+# If(f) the output of a state is different than the one from our base state,
+# then, we encode that in a new variable. This is only needed when the base
+# state is active, so the first literal in these clauses is -bvar(base).
+for s in tqdm(states, desc="diff1"):
+  for base in bases:
+    if s == base:
+      continue
+    bv = bvar(base)
+    for i in range(length):
+      for o in outputs:
+        # x_('o', state, i, o) /\ -x_('o', s, i, o) => x_('e', s, i)
+        # == -x_('o', state, i, o) \/ x_('o', s, i, o) \/ -x_('e', s, i)
+        solver.add_clause([-bv, -ovar(base, i, o), ovar(s, i, o), evar(s, i)])
+
+        # We also need the other direction, we can do this:
+        # x_('e', s, i) /\ x_('o', state, i, o) => -x_('o', s, i, o)
+        # == -x_('e', s, i) \/ -x_('o', state, i, o) \/ -x_('o', s, i, o)
+        solver.add_clause([-bv, -evar(s, i), -ovar(base, i, o), -ovar(s, i, o)])
+
+# Now we have to say that the other state have some different output on their path
+for s in tqdm(states, desc="diff2"):
+  # constraint: there is a place, such that there is a difference in output
+  # \/_i x_('e', s, i)
+  # If s is our base, we don't care
+  if s in bases:
+    solver.add_clause([bvar(s)] + [evar(s, i) for i in range(length)])
+  else:
+    solver.add_clause([evar(s, i) for i in range(length)])
+
+measure_time('Constructed CNF with', solver.nof_clauses(), 'clauses and', solver.nof_vars(), 'variables')
+
+
+# Solving
+
+for s in bases:
+  print('*** UIO for state', s)
+  b = solver.solve(assumptions=[bvar(s)])
+  measure_time('Solver finished')
+
+  if b:
+    m = solver.get_model()
+    truth = set()
+    for l in m:
+      if l > 0:
+        truth.add(l)
+    
+    print('! word')
+    for i in range(length):
+      for a in alphabet:
+        if avar(i, a) in truth:
+          print(a, end=' ')
+    print('')
+
+    if verbose:
+      print('! paths')
+      for s in states:
+        print(s, '=>', end=' ')
+        for i in range(length):
+          for t in states:
+            if svar(s, i, t) in truth:
+              print(t, end=' ')
+        print('')
+      
+      print('! outputs')
+      for s in states:
+        print(s, '=>', end=' ')
+        for i in range(length):
+          for o in outputs:
+            if ovar(s, i, o) in truth:
+              print(o, end=' ')
+        print('')
+
+      print('! differences')
+      for s in states:
+        if s == base:
+          continue
+        print(s, '=>', end=' ')
+        for i in range(length):
+          if evar(s, i) in truth:
+            print('x', end='')
+          else:
+            print('.', end='')
+        print('')
+
+  else:
+    print('! no word')
+    core = solver.get_core()
+    print(core)