This is the beginning of a series of expositions on the topic of machine-assisted theorem proving. My goal is to write the expositions in an entertaining and educational format so the layperson can understand the motivation, difficulty, and possible solutions in the quest for what I call logical creativity, which has been a very tough goal for artificial intelligence systems.

Search

Many tasks in everyday life involve search. Planning a schedule, playing a game of Go, or packing a bag all boil down to finding an optimal configuration in the current state. Search is a cycle with the following steps:

1. Find the possible actions on a state
2. Compute the result of this action
3. Return to step (1) with derived states from step (2)

Picnic

Let’s consider a planning question. Five friends want to have a picnic. Their names are Letty, Chen, Alice, Merlin, and Yomu, and five dishes: fish, soup, dessert, bread, and fruit.

There are some restrictions:

1. Each person can only cook one dish
2. Fruit grows in the forest. It must be brought to the picnic. Letty lives far away from the forest so she prefers not to do this unless necessary (foreshadowing). Yomu is afraid of the dark so she cannot go as well.

Each person can only prepare certain dishes:

Given these restrictions, what should each person prepare for the picnic?

Solution

A common solution for a problem of this scale is to enumerate every single combination of person and dish. While it is a sensible method for smaller problems, the number of possible combinations quickly explode in size as the number of questions get large. In fact it grows as $O(n!)$ for combinatorial problems of size $n$. For problems involving numbers, there may be infinite combinations.

A brute force solution will not scale, so we turn to the next best method of solving this problem: Try to solve it one small piece at a time.

We could start by assigning Merlin the task of cooking dessert. At this point, we do not know if this assignment will be correct in the end, but we must make some attempt to reach a fuller solution.

Then we could try assigning Chen the task of bread.

This leaves not many options for the remaining members. Letty then must be cooking soup.

But this is a problem. There is no dish Yomu can cook now! If every person can only cook one dish, Yomu must do something. Recall that our decisions about what the tasks of Merlin and Chen were arbitrary, so we could retrace our step back to Chen’s decision. This is known as backtracking. Suppose Chen this time cooks Fish.

Learning from our mistakes (foreshadowing), we assign Letty to the task of bread.

At this stage, the tasks of Alice and Yomu have been narrowed down so they do not affect each other. Thus we conclude the roles of each person are

Formulation

The above search process can be rigorously formulated in a proof assistant. A proof assistant is a computer program which automatically checks the correctness of logic. Examples include Lean, Isabelle, Coq, and Aya. In Lean 4, every statement, theorem, proof, or function is an expression. For example, we can write a function that maps a natural number to another natural number:

λ (x: Nat) ⇒ 3 * x + 2

$\vdash$ is the entailment operator, indicating the type of the left side is equal to the right side. Each valid expression has a property called type that is another expression. This article will not go into exactly what is a type. Readers who are interested should read Calculus of Inductive Constructions in Coq’s documentation. The type of this expression is

Nat → Nat

which is the type of all functions that maps a natural number to another natural number.

To represent the picnic task, we could define a type that has just 5 values:

inductive PicnicTask where
  | fish
  | bread
  | dessert
  | fruit
  | soup
deriving instance DecidableEq for PicnicTask

The DecidableEq instance is a technicality in Lean which allows us to compare two PicnicTasks.

For example, the statement “Cirno can cook all five dishes” can be written in Lean as

?Cirno : PicnicTask

This means the value of ?Cirno can only be one of the five cases listed in PicnicTask.

and “Letty can cook soup or bread” is

?Letty : { t: PicnicTask // [.soup, .bread].contains t }

This is subtyping, a construction in Lean where the type of a variable is restricted to the set of another type. ?Letty.val is now restricted to either .soup or .bread.

?Letty is a unassigned metavariable or a goal. Lean 4 uses metavariables to drive the proof search process. The user’s task is to provide a solution to every goal. In Lean, the user assigns values to goals by directly writing out a proof expression or use commands called tactics. A tactic generates an expression dependent on the current goal.

The picnic problem above can be written as a list of goals:

?Chen   : { t: PicnicTask // [.bread, .fish, .fruit].contains t }
?Alice  : { t: PicnicTask // [.fruit, .dessert, .fish].contains t }
?Letty  : { t: PicnicTask // [.soup, .bread].contains t }
?Merlin : { t: PicnicTask // [.fruit, .dessert].contains t }
?Yomu   : { t: PicnicTask // [.dessert, .soup].contains t }
?unique : ?Chen.val ≠ ?Alice.val ∧ ?Chen.val ≠ ?Letty.val ∧ ... ∧ ?Merlin.val ≠ ?Yomu.val

Or, written mathematically

When we begun to search for the optimal picnic arrangement, we did not have a fully clear picture of the solution, represented by the question marks. Yet we can still reference these indeterminant values in statements such as “whoever makes the dessert cannot also make fruit”.

In particular the assignments of metavariables affect each other. If $?\text{Chen} := \text{Bread}$, then we cannot have $?\text{Letty} = \text{Bread}$. What we observed above is a phenomenon called Metavariable Coupling and the terminology was first introduced in Aesop.

Metavariable coupling often arise in mathematical problems. A minimal metavariable coupling example is the following. Consider proving $2 \leq 5$. Set this to a goal $?1$ and suppose we use $\leq$’s transitivity to prove it. This amounts to proving

Not only do we have to exhibit some value $z$, we also need to prove that it is between $2$ and $5$! In an example like this, it is straightforward to try a value of $z$ such as $z := 3$. This decouples the two goals $?1$ and $?2$:

so their solutions no longer affect each other.

Solutions of Metavariable Coupling

Metavariable coupling arises when we try to use artificial intelligence to solve mathematics. It does not appear often, and hence some research papers could get away with not mentioning it, but it is a fundamental feature of the logic system we use. Aesop is a Lean proof automation tool and it first provided a concrete definition and solution to coupling. Aesop’s solution to coupling is copying. When a goal is solved via a tactic execution, all the goals that were coupled to it are copied (called resumption) as children goals.

In our tool Pantograph, the user has the choice on how to handle metavariable coupling. The user has the choice to decide when to resume a goal and continue from an earlier proof state. This is named Continuation-Resumption Paradigm. Customized handling makes it easier to refute fruitless search branches on the spot.

In the next post, we’ll see the a class of powerful tools for solving coupled reasoning problems: Satisfiability Modulo Theory (SMT) Solvers.