Let's look at the following problem: f(a, b, c, x, y, z) is a Boolean function where a, b, c, x, y and z are Boolean values, and the output of f is a boolean value. The definition of f consists of a number of and/or/nor operators. I want to find a set of three Boolean values x0, y0, and z0, such that:
f(0, 0, 0, x0, y0, z0) = 0 AND
f(0, 0, 1, x0, y0, z0) = 1 AND
f(0, 1, 0, x0, y0, z0) = 1 AND
f(0, 1, 1, x0, y0, z0) = 0 AND
...
f(1, 1, 1, x0, y0, z0) = 1 # A total of 8 constrains. Each of them is from an entry in the truth table.
A naive approach is to define 3 Boolean variables x, y, z, and repeatedly define the function f 8 times. However f consists of complex boolean expressions. Defining it 8 times can blow up the model. Moreover, in fact, I have 7 variables: a, b, c, d, e, f, g. The truth table has 128 entries.
Is there a way to define something like a "placeholder" variables a, b, c which are not part of the solution? I can then define f on a, b, c, x, y, z only once, and later somehow "assign" a, b, c to Boolean constants.
I'm a newbie on SMT solvers, the placeholder idea might be totally irrelevant. Other solutions are appreciated as well.
It's hard to decipher exactly what you are trying to do. But it seems to me that your concern is about how to code this easily, as opposed to anything else. If so, I'd recommend programming in a language that provides a higher-level API than SMTLib. You can use many of the interfaces z3 supports: C, C++, Java, etc., to simplify the programming task.
For instance, here's how one can use the Python interface to code an instance of your problem:
from z3 import *
def fOriginal(a, b, c, x, y, z):
return Or([a, b^c, x, y, z])
x0, y0, z0 = Bools("x0 y0 z0")
def f(a, b, c):
return fOriginal(a == 1, b == 1, c == 1, x0, y0, z0)
s = Solver()
s.add(f(0, 0, 0) == False)
s.add(f(0, 0, 1) == True)
s.add(f(0, 1, 0) == True)
s.add(f(0, 1, 1) == False)
s.add(f(1, 1, 1) == True)
print(s.check())
print(s.model())
When run, this prints:
sat
[y0 = False, z0 = False, x0 = False]
Giving you an assignment to x0, y0, and z0 as you're trying to find.
The idea here is to code f as a regular function, using z3's interface. I called this function fOriginal in the python code. We then define a version of fOriginal, which I called f in the code, which passes symbolic values for the last three arguments, but expects constants for the first three.
We then simply add the constraints for each of your cases. I added only 5 above following your example; you can add all 8 of course.
I hope this gets you started!
It's not clear to me what you need the model for and how much flexibility you need, but here is an idea: if it is acceptable to constrain the function on all argument values, via a single else-case, then the following might work:
(declare-fun f (Bool Bool Bool Bool) Bool)
(assert (forall ((x Bool) (y Bool) (z Bool) (p Bool)) (!
(=
(f x y z p)
(ite
(and (not x) y) true (ite ; 1. case
(and (not x) (not y) z) true (ite ; 2. case
(and x (not y) z) true ; 3. case
false)))) ; else-case
:pattern ((f x y z p)))))
At least for this simple case, Z3's model effectively ignored the irrelevant 4th parameter of function f:
(check-sat) ;; SAT --> good
(get-model)
; NOTE: 4th parameter x!3 is declared, but not used
; (model
; (define-fun p () Bool
; false)
; (define-fun f ((x!0 Bool) (x!1 Bool) (x!2 Bool) (x!3 Bool)) Bool
; (or (not (or x!0 (not x!1)))
; (not (or x!1 (not x!2) (not x!0)))
; (not (or x!1 x!0 (not x!2)))))
; )
Is it possible to treat int constants as uninterpreted in z3? For example, treat tuple(project(t, 0), project(t, 1)) = t as tuple(project(t, left), project(t, right)) = t. Context: my equations are essentially in QF_UF, but because they contain int constants I'm forced to use a logic with integer arithmetic which leads to nontermination sometimes.
You can declare Int to be an uninterpreted sort, so long as you set the logic to be something that doesn't define it already:
(set-logic QF_UF)
(declare-sort Int 0)
(declare-fun f ((Int)) Int)
(declare-fun i () Int)
(assert (distinct (f i) (f i)))
(check-sat)
z3 says:
unsat
If you add:
(assert (= (f 2) 2))
then you get:
(error "line 8 column 15: Sort mismatch at argument #1 for function (declare-fun f (Int) Int) supplied sort is Int")
which avoids confusions. (Though the error message is rather confusing to read!)
If you set your logic to be:
(set-logic QF_LIA)
then z3 says:
(error "line 3 column 18: sort already defined Int")
so, that works out as well. See Section 4.2.3 of the SMTLib specification for details.
Hope that helps!
I have a program that sorts variables, and I'm trying to check its validity with Z3, but I have one code segment where the variables are being swapped and I don't know how to model it in SMT syntax. Here is the original code segment:
if (x > y) {
temp = x;
x = y;
y = temp;
}
And regarding the SMT I have written an assertion, but I guess it is not exactly the correct thing:
(declare-fun x () Int)
(declare-fun y () Int)
(declare-fun temp () Int)
(assert (=> (> s1 s2) (and (= tmp s1) (= s1 s2) (= s2 tmp))))
Any ideas how to do variable assignment in SMT?
You should look into Single Static Assignment [1]. In this way you can rewrite your original code as follows.
if (x0 > y0) {
temp0 = x0;
x1 = y0;
y1 = temp0;
}
It thus becomes clear that you have two different instances of x and y. The first (x0, y0) is the one you are comparing in the if condition. The second (x1, y1) is the result of the operation.
This introduces an implicit notion of time that makes it also easier to write properties about your code. E.g.,
((x1 = x0) & (y1 = y0)) | ((x1 = y0) | (y1 = x0))
Of course, this might require adjusting other parts of your code, so that you are using the right variables.
[1] https://en.wikipedia.org/wiki/Static_single_assignment_form
We can rewrite what you want using a single expression as a tuple:
(result1, result2) = x > y ? (x, y) : (y, x)
Z3 supports tuples but I'm less experienced with that. It's probably easier to blast this into parts:
result1 = x > y ? x : y
result2 = x > y ? y : x
And the ?: operator maps to ITE in Z3.
You don't even need "temp variables" for this (but clearly you can).
(assert (=> (> s1 s2) (and (= tmp s1) (= s1 s2) (= s2 tmp))))
I think this is revealing that you don't understand that Z3 "variables" are actually constants and you cannot actually swap them. In each model they take on a single value only. There is no temporal component to constants. = means "is equal?" and not "make it equal!".
I'm trying to influence the randomness of results for model values generated by Z3. As far as I understand, the options for this are very limited: in case of linear arithmetic, the simplex solver does not allow for random results that still satisfy the given constraints. However, there is an option smt.arith.random_initial_value ("use random initial values in the simplex-based procedure for linear arithmetic (default: false)") which I don't seem to get working:
from z3 import *
set_option('smt.arith.random_initial_value',True)
x = Int('x')
y = Int('y')
s = Solver()
s.add( x+y > 0)
s.check()
s.model()
This seems to always produce [y = 0, x = 1] as a result. Even model completion for variables unused in the given constraints seems to produce deterministic results all the time.
Any ideas or hints about how this option works?
Thanks for catching that! There was indeed a bug that caused the random seed not to be passed through to the arithmetic theory. This is now fixed in the unstable branch (fix here).
This example:
(set-option :smt.arith.random_initial_value true)
(declare-const x Int)
(declare-const y Int)
(assert (> (+ x y) 0))
(check-sat-using (using-params qflra :random_seed 1))
(get-model)
(check-sat-using (using-params qflra :random_seed 2))
(get-model)
(check-sat-using (using-params qflra :random_seed 3))
(get-model)
Now produces three different models:
sat
model
(define-fun y () Int
4294966763)
(define-fun x () Int
4294966337)
)
sat
(model
(define-fun y () Int
216)
(define-fun x () Int
4294966341)
)
sat
(model
(define-fun y () Int
196)
(define-fun x () Int
4294966344)
)
It looks like there may be another place where this option isn't passed through correctly (e.g., when using set-logic instead of calling the qflra tactic directly), we're still looking into that.
I am trying to remove existential quantifiers in my theory using Skolemization. This means that I replace existential quantifiers with functions that are parameterized by the universally quantified variables in the scope of the existential quantifiers.
Here I found an explanation how to do this in Z3, but I am still having troubles doing it. Suppose the following two functions:
(define-fun f1 ((t Int)) Bool (= t 3))
(define-fun f2 () Bool (exists ((t Int)) (f1 t)))
I believe that f2 should be true, because there exists an integer t such that (f1 t) is true, namely t=3. I apply Skolemization by introducing a constant for the existentially quantified formula:
(define-const c Int)
Then the formula with the existential quantifier is rewritten to:
(define-fun f2 () Bool (f1 c))
This does not work, that is, the constant c does not have the value 3. I suspect it is because we have not given an interpretation to the constant c, because if we add (assert (= c 3)) it works fine, but this takes away the whole idea of the existential quantifier. Is there a way in which I give a less explicit interpretation to c so that this will work?
So, I think you have it about right actually, here's the script I used with automatic (via Z3's SNF tactic) and manual (via adding the constant c) skolemization, which gave the value 3 in the model for the skolem constant as expected (smt-lib script: http://rise4fun.com/Z3/YJy2 ):
(define-fun f1 ((t Int)) Bool (= t 3))
(define-fun f2 () Bool (exists ((t Int)) (f1 t)))
(declare-const c Int)
(define-fun f2a () Bool (f1 c))
(push)
(assert f2)
(check-sat) ; sat
(get-model) ; model gives t!0 = 3 (automatic skolemization in Z3)
(pop)
(push)
(assert f2a)
(check-sat) ; sat
(get-model) ; model gives c = 3 after manual skolemization
(pop)
Also, note that Z3 has a Skolem normal form (SNF) conversion tactic built in, and here's an example in z3py (link to script: http://rise4fun.com/Z3Py/ZY2D ):
s = Solver()
f1 = Function('f1', IntSort(), BoolSort())
t = Int('t')
f2 = Exists(t, f1(t))
f1p = ForAll(t, f1(t) == (t == 3)) # expanded define-fun macro (define-fun f1 ((t Int)) Bool (= t 3))
s.add(f1p)
s.add(f2)
print f1p
print f2
print s.check()
print s.model() # model has skolem constant = 3
g = Goal()
g.add(f1p)
g.add(f2)
t = Tactic('snf') # use tactic to convert to SNF
res = t(g)
print res.as_expr()
s = Solver()
s.add( res.as_expr() )
print s.check()
print s.model() # model has skolem constant = 3