Meet the Robinson: 2

Thursday, November 5, 2015 · 6 min read

Welcome back.

In the previous article, we learned how rules of inference allow us to prove theorems. We also learned that a rule of inference is not necessarily complete: it might be impossible to prove a true sentence with a valid rule of inference.

Today’s article is about a complete rule of inference.

Before you get excited, though, let’s talk about what we really mean by “complete” in this case.

  1. If a statement must be true given some axioms, then the rule of inference can construct a proof for the statment.
  2. If a statement is false given some axioms, then the rule of inference will not construct a proof. It is not guaranteed to even terminate in this case.
  3. If a statement could either be true or false given some axioms, then the rule of inference will not construct a proof. It is not guaranteed to even terminate in this case.

  4. Prove that “Robert is a frog” given “It rained on Monday” and “Seven is a prime number”.

  5. Prove that “Robert is not a frog” given “It rained on Monday” and “Seven is a prime number”.
  6. Explain what we mean by statements that could either be true or false, and connect this to the discussion of undecidable problems (or independent statements) in the previous article. These statements are (choose one) satisfiable/valid but not satisfiable/valid.
  7. How can you use this rule of inference to prove that something is false?
  8. How can you use this rule of inference to discover whether a statement is true or false, given that it is necessarily either valid or invalid? (Hint: you’re allowed to fork two processes and kill them if needed.)

For the purposes of this article, we can assume that the algorithm will terminate. Later, though, we will generalize to higher forms of logic where termination is not guaranteed.

The rule of inference we are going to use is called resolution, first used practically by John Alan Robinson in 1965. It goes like this:

\[ \frac{(P \vee \neg Q), (Q \vee R)}{P \vee R} \]

Resolution proofs rely on the idea of refutation or reductio ad absurdum, which is more commonly known as proof by contradiction.

Refutation relies on the idea that if not-S causes a contradiction with your axioms and rules of inference, then not-S is invalid, so S must be valid.

To use the resolution-refutation scheme to prove S given some axioms, we can repeatedly apply resolution to pairs of sentences taken from the axioms and not-S. If you end up deriving a contradiction, you have proven S.

The rest of this post will be dedicated to proving that this scheme actually works.

Resolution operates on clauses, which is just a set of propositions that are joined with “or”s. These propositions can be negated.

Sentences are in clausal-normal form (CNF) if they are a set of clauses that are joined with “and”s.

It turns out that any sentence can be written in CNF. To do this, you can repeatedly apply the following rules:

  1. Remove implication using the expression for “A ⇒ B” you’ve already found.
  2. Use De Morgan’s laws to move “not” inwards:
    • What is ¬ ¬ A?
    • What is ¬ (A ∧ B)?
    • What is ¬ (A ∨ B)?
  3. Distribute ∨ over ∧ by replacing A ∨ (B ∧ C) with (A ∨ B) ∧ (A ∨ C).
    • Prove that this is true with a truth table.

You can use induction to show that applying these rules again and again will eventually turn your sentence into CNF.

Why do we care so much about sentences in CNF? We can now extend the resolution rule to sentences in CNF:

\[ \frac{(P_1 \vee \dots \vee P_{i} \vee \neg X), (Q_1 \vee \dots \vee Q_{i} \vee X)}{P_1 \vee \dots \vee P_{i} \vee Q_1 \vee \dots \vee Q_{i}} \]

In short, if a proposition appears in both positive and negative polarities in two clauses, you can join the clauses and remove that proposition.

We’re now going to prove if you have an unsatisfiable set of clauses, then you can repeatedly resolve them to derive the empty clause. This provides a model for us to have a terminating theorem-proving algorithm!

This proof is taken almost directly from Russell and Norvig’s textbook, though I tried to cut down on the notation.

Imagine we’ve already applied resolution to each pair of clauses, and recursively resolved the results, and so on to obtain an (infinite?) set of clauses. We call this the resolution closure of these clauses.

Suppose also that the set of clauses was unsatisfiable, but this set of resolved clauses does not contain the empty clause. We’re going to show that you can, in fact, satisfy the clauses, leading to a contradiction.

To satisfy the clauses, follow the following algorithm:

  1. Number your propositions from 1 to k.
  2. For the ith proposition P[i]:
    1. If one of the clauses in the set contains ¬P[i], and all other propositions in that clause are false based on the values assigned to P[1], P[2], …, P[i-1], then set P[i] to false. This is basically clauses that look like (falsefalse ∨ … ∨ ¬ P[i]).
    2. Otherwise, set P[i] to true.

The claim is that this procedure will always work, that is, none of these assignments will make a clause false. To see this, suppose assigning P[i] did, in fact, cause a clause to be false. Specifically, no clauses were falsified by any of the previous assignments. Then the following could happen:

  1. That clause was empty from the beginning, and thus always false. We can ignore this case because we’re assuming the set of resolved clauses does not contain the empty clause.
  2. The clause was of the form (falsefalse ∨ … ∨ P[i]) and we assigned P[i] to false. (Why is this the only other case?)

If the latter case occurs, then we must also have (falsefalse ∨ … ∨ ¬ P[i]), because that’s the only situation in which P[i] is assigned false.

Now, here’s the crucial bit: these two clauses resolve! Since they contain P[i] and ¬P[i], respectively, we resolve them to obtain a clause that has only false values in it. This contradicts our assumption that no clauses were falsified by any of the previous assignments.

So, we know that if the empty clause is not part of the set of resolved clauses, then the clauses are satisfiable. By contrapositive, it follows that you can always resolve some set of them to get the empty clause. Q.E.D. ■

There’s another argument that explains this: an inductive one which you can read here. In short, it inducts on the total number of propositions, where the base case is that if you have only one literal per clause, then either it’s satisfiable, or it’s unsatisfiable and you can resolve to get the empty clause.

So where are we? We have an algorithm that will definitely terminate on a valid statement, though we have not yet said anything about what happens when it is given anything else.

The resolution method should feel extremely powerful. Of course, as the number of propositions increases, it will get exponentially slower (boolean satisfiability was actually one of the first problems to be proved as NP-complete—and, in fact, constraint solving algorithms like DPLL correspond pretty much to resolution-refutation).

But that’s okay! It works!

One question you should have is, “What can I prove with propositional logic?”

Propositional logic is bound to a finite number of propositions. That means you can’t say things like “1 is an integer”, “2 is an integer”, “3 is an integer”, etc.

In the next article, we will extend propositional logic to a much richer form that supports an infinite number of propositions, and show that resolution-refutation still works.

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