B2SAT

The current versions of ProB can make use of the new B2SAT backend as an alternate way of solving constraints. It translates a subset of B formulas to SAT and for solving by an external SAT solver. The new backend interleaves low-level SAT solving with high-level constraint solving performed by the default solver of ProB.

Using B2SAT

B2SAT in the REPL

The following four commands are now available in REPL of probcli (start with probcli --repl):

• :sat PRED
• :sat-z3 PRED
• :sat-double-check PRED
• :sat-z3-double-check PRED

Here is a simple example:

 >>> :sat f:1..n --> BOOL & n=3 & f(1)=TRUE & !i.(i:2..n => f(i) /= f(i-1))
 PREDICATE is TRUE
 Solution: 
        f = {(1|->TRUE),(2|->FALSE),(3|->TRUE)} &
        n = 3

B2SAT for PROPERTIES

The new preference SOLVER_FOR_PROPERTIES can be used to specify solver for PROPERTIES (axioms) when setting up constants. The valid settings are: prob (the default), kodkod, z3, z3cns, z3axm, cdclt, sat, sat-z3.

The last two will use B2SAT, {\tt sat} with Glucose as SAT solver and {\tt sat-z3} with Z3 as SAT solver.

Here is an example using this preference:

MACHINE IceCream_Generic
// Dominating set example:
// place ice cream vans so that every house (node) is at most one block away from a van
// a version which can be solved by B2SAT
DEFINITIONS
 N == 24;
 CUSTOM_GRAPH == rec(layout:"dot", rankdir:"TB",
                     nodes: {j•j:NODES |
                               rec(value:j, style:"filled",
                                fillcolor:IF ice(j)=TRUE THEN "mistyrose" ELSE "white" END
                                  )},
                    edges:rec(color:"gray", arrowhead:"odot",
                              arrowtail:"odot", dir:"both",
                              label:"edge",   edges: edge)
                    );
      bi_edge == (edge \/ edge~);
  SET_PREF_SMT == TRUE;
  SET_PREF_TIME_OUT == 50000;
  SET_PREF_SOLVER_STRENGTH == 300;
  SET_PREF_SOLVER_FOR_PROPERTIES == "sat";
SETS
  NODES = {n1,n2,n3,n4,n5,n6,n7,n8,n9,n10,
           n11,n12,n13,n14,n15,n16,n17,n18,n19,n20,n21,n22,n23,n24}
CONSTANTS edge, ice, vans, neighbours
PROPERTIES
 edge: NODES <-> NODES &
 edge = { n1|->n2, n1|->n4,
          n2|->n3,
          n3|->n4, n3|->n5, n3|->n7,
          n4|->n7,
          n5|->n6, n5|->n9,
          n6|->n7, n6|->n8,
          n7|->n8,
          n8|->n10, n8|->n13,
          n9|->n10, n9|->n11, n9|->n12,
          n11|->n12, n11|->n14,
          n12|->n13,
          n13|->n16,
          n14|->n15, n14|->n17,
          n15|->n16, n15|->n17, n15|->n18, n15|->n21,
          n16|->n18, n16|->n19,
          n17|->n19,
          n18|->n19, n18|->n20, n18|->n21,
          n19|->n20, n19|->n21,
          n20|->n21, n20|->n22,
          n21|->n22, n21|->n23, n21|->n24,
          n22|->n23, n21|->n24,
          n23|->n24
          } &
 ice : NODES--> BOOL &
 neighbours = %x.(x:NODES|bi_edge[{x}]) &
 !x.(x:NODES =>
      (ice(x)=TRUE or
        #neighbour.(neighbour: neighbours(x) & ice(neighbour)=TRUE)
                 )
    )
 & vans = card(ice~[{TRUE}])
 & card({x|x:NODES & ice(x)=TRUE})<=6  /* minimal solution requires 6 vans */
OPERATIONS
  v <-- NrVans = BEGIN v := vans END;
  xx <-- Get(yy) = PRE yy:NODES THEN xx:= ice(yy) END;
  v <-- Vans = BEGIN v:= ice~[{TRUE}] END
END

Here is the graphical rendering of a solution using the custom graph definition above:

File:IceCream Generic.pdf

B2SAT in Jupyter Notebooks

This shows how you can use cardinality constraints to iteratively find an optimal solution: