Axiomatic is a logic framework that, given declarations of facts and rules, provides a mechanism to run a query to determine if a statement is true, and if so, under what conditions. Axiomatic is built on top of the unification framework Gluey, and it extends it by defining tree-like unification types as well as a query system that is suitable for logic programming. Axiomatic is closely based off the logic programming language Prolog as well as the mathematic concept of Horn clauses.
Rather then asking you to write a step-by-step algorithm to solve a problem, logic programming asks you to provide a set of facts and rules. For example, we can starting by giving the fact that grass is green color(grass, green) and the rule that green things are awesome awesome(X) :- color(X, green), and then query whether grass is in fact awesome, ?- awesome(grass). Note that the order of "grass" and "green" are unimportant as long as we are internally consistent. Logic programming doesn't just let us ask yes or no questions though, we can ask it for all possible solutions that fit our set of constraints.
Consider the following facts and rules (represented as a Prolog program):
parent(matt, jaden).
parent(tuesday, jaden).
parent(debbie, matt).
parent(dennis, matt).
parent(liz, tuesday).
parent(mike, tuesday).
grandparent(A, B) :- parent(A, X), parent(X, B).We can easily query to find out who is the grandparent of Jaden, ?- grandparent(G, jaden), and with the response that G is either Debbie, Dennis, Liz, or Mike, as we'd expected.
Logic programming provides a really simple mechanism to find answers that can be easily deduced from a set of rules. There's no need to worry about enumerating all possible matches or even backtracking yourself as the framework handles it all.
Terms is the most primitive logic type provided by Axiomatic. Essentially, it allows you define both atoms, like jaden and green, as well as complex compound terms, such as awesome(jaden) or triangle(point(0, 0), point(1, 1), point(0, 1)). Terms consist of a name as well as 0 or more arguments. Though the name must be a literal value, the arguments may be variables. For example, color(X, purple) talks about everything that is purple!
let s = Term(name: "cool", arguments: [.Literal(Term(atom: "swift"))]) // cool(swift).
let p = Term(name: "cool", arguments: [.Literal(Term(atom: "prolog"))]) // cool(prolog).Note that each argument of a Term is of type Unifiable<Term>, so you must specify if the argument is of the Unifiable.Literal(Term) or the Unifiable.Variable(Binding) case. As a reminder, a Binding is a type defined by Gluey that can be unified with other instances of the same type. It is used to represent variables within this framework since they become bound together by the unification process and often two variables in separate terms ought to refer to the same value.
Clauses make statements of the form X implies Y. That Y is called the head of the clause, and it consists of a single term, while that X is called the tail of the clause, and it consists of a collection of terms that when true, imply the head is true. The special case in which the tail is empty is called a fact since it is unequivocally true. Otherwise, a clause is called a rule since the tail defines a sufficient condition upon which the head will be considered true.
As a reminder, a Clause is formed entirely of our Terms. The clause happy(monkey) :- eating(monkey, banana) for example, says that the term happy(monkey) is true whenever the term eating(monkey, banana). It doesn't however imply the converse since there might exist another clause that says the monkey is also happy if it's rope swinging.
Clauses can and often do utilize terms with variable arguments to specify conditional truths. This is done by declaring a Binding and using it as a variable in one or more arguments in one or more terms of the clause. Note that it is illegal but unchecked to share the same Binding between multiple variables in separate clauses, and doing so will result in undefined behavior.
// awesome(X) :- color(X, green).
let x = Binding<Term<String>>()
let c = Clause(
rule: Term(name: "awesome", arguments: [
.Variable(x)
]),
conditions: [
Term(name: "color", arguments: [
.Variable(x),
.Literal(Term(atom: "green"))
])
]
)Now you're probably thinking, wow, that's a really wordy definition of such a simple Prolog query, and you're right. Axiomatic isn't intended to be used to build programs "out of the box", but rather it's intended to be used a base for programs that rely on logic. Further, it relatively easy and straightforward to provide an abstraction atop Axiomatic to make it suitable for specific use cases.
Once you've defined clauses to your heart's desire, you're ready to finally do something with them. System provides an initializer that takes in a sequence of clauses and builds a logic system that can be easily queried. Let's check out what our grandparent example from above looks like as an Axiomatic system!
let system = System(clauses: [
// parent(matt, jaden).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Matt")),
.Literal(Term(atom: "Jaden"))
])),
// parent(tuesday, jaden).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Tuesday")),
.Literal(Term(atom: "Jaden"))
])),
// parent(debbie, matt).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Debbie")),
.Literal(Term(atom: "Matt"))
])),
// parent(dennis, matt).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Dennis")),
.Literal(Term(atom: "Matt"))
])),
// parent(liz, tuesday).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Liz")),
.Literal(Term(atom: "Tuesday"))
])),
// parent(mike, tuesday).
Clause(fact: Term(name: "parent", arguments: [
.Literal(Term(atom: "Mike")),
.Literal(Term(atom: "Tuesday"))
])),
// grandparent(A, B) :- parent(A, X), parent(X, B).
Clause{ A, B, X in (
rule: Term(name: "grandparent", arguments: [.Variable(A), .Variable(B)]),
conditions: [
Term(name: "parent", arguments: [.Variable(A), .Variable(X)]),
Term(name: "parent", arguments: [.Variable(X), .Variable(B)])
]
)}
])Damn, that was long! Well, don't worry about that. As we said, syntactical conciseness was never a goal! So what did we just do? We defined a System of logical facts and rules that we can later query.
Notice that the initalizer for our grandparent rule took in a lambda? Well, Axiomatic defines these sorts of convenience initializers for Clause so you can define rules without having to separately declare a Binding. Simply pass a lambda taking as many Binding arguments as you'd like (up to 6) into the initializer for Clause, and return the argument tuple it'd normally expect. If you're confused don't worry, this is just a syntactic convenience; you can still declare your bindings separately in the outer scope.
So how do we query this system? Well, there's a fancy little function called enumerateMatches just for this!
let G = Binding<Term<String>>()
let query = Term(name: "grandparent", arguments: [.Variable(G), .Literal(Term(atom: "Jaden"))])
try system.enumerateMatches(query) {
print(G) // -> Debbie -> Dennis -> Liz -> Mike
}Ta-dah! Pretty simple, huh? Notice that we had to call that function with a try. This is because our query may fail to unify at all. Futhermore, you're not guaranteed that every Binding will have a non-nil value inside of the callback. If the logical system doesn't place sufficient restrictions on a variable, it may be possible for it to be unified without finding an actual concrete value.
By default, enumerateMatches will call the callback for each possible match. If you'd instead like to return after any given match, simply throw SystemException.Break, and the system will halt the unification process.
You are not guaranteed that the unified state of the variables will remain after you return from the callback. As such, make sure to record any information you might need to know while inside the callback.
So how does this fancy schmancy enumerateMatches function work anyhow? Well, it uses a process known as unification by which all the possible matches are enumerated and attempted, recursively querying the dependencies of a clause on success and backtracking on failure. We'll look at the algorithm in a bit more detail. If you're just looking to use this library, feel free to skip this section.
The first step is to, given a goal, determine what possible clauses we might be able to unify with. Any clause with the same functor (name and arity) as our clause is a potential candidate for unification. Luckily, System stores a dictionary of type [Functor : [Clause]] so it's efficient to look up a list of clauses compatible with a given term.
For each suitable candidate, we will attempt to unify the head of the clause with our query. If unificaiton fails, we've determined that they are incompatible, and we move onto the next clause. If unification with the head succeeds, we've determined that this clause provides insight to this query. Recall that the head of a clause is only true if each term of its body is also true. Thus, if we are able to unify its body, we can consider the query unified with this clause. Therefore, we must recursively call enumerateMatches on each term of its body.
If we are able to unifiy all of these terms in the body, then we should invoke the callback. To do this, we set the callback of the first term to unify the second term, and that term's callback to unify the third term, and so on. The callback of the final term will call the original passed in lambda, so it will be called once all terms in the body have been unified.
On failure at any point, we backtrack to the last choice point and continue from there. Similiarly, after the caller has been notified of a successful match, we simulate an error occuring such that we'll again backtrack to the last choice point so we can find the next match. Note that this backtracking involves both popping the call stack and restoring a previous unification state. The former is done using Swift's efficient exception handling while the latter is done by saving unificaiton state snapshots at choice points and later catching exceptions that occur so that this snapshot can be restored.
Overall, the process for finding matches is pretty simple! Just look at all the clauses a query might match with, attempt to unify it with the clauses head, and then unify with the body of the clause. If all this succeeds, we have a match! If not, keep looking! Check out the source code if you'd like to see more!
If you're interested in how the unification of two single terms is performed, you should first read the documentation for Gluey as its a minor extension of the functionality provided by that module. In fact, the only modification by Axiomatic is the introduction of a Term type which is a tree-like datastructure. Since Unifiable values in Gluey already support recursive unfication, when two terms are unified, it simply attempts to unify each of their arguments. Super simple! If you don't believe me, here's the definition in the source!
Hopefully this was a good introduction to logic programming and the Axiomatic framework. If you find yourself still confused, dig through the source code a bit, check out some of the test cases, and maybe read the documentation. If you're still lost, feel free to tweet me! :)